Getting in front of pharma: Automated public discovery of drug candidates

A couple weeks ago Sean and I were fortunate enough to participate in another edition of Rhizome's 7x7, this time in Beijing in collaboration with the Chinese Central Academy of Fine Arts (CAFA). I was very excited to collaborate with Sean again after our first collaboration in New York at the New Museum, and to have the chance to try something different together.

We thought about revisiting our previous project,, which was a proposal for a cryptocurrency/distributed computing system for which the proof-of-work protocol involved computing simulation updates for an atomic-level model of a human cell (though our proposal initially suggested simulating a ribosome). Such detailed simulation of biological processes would be a boon for medical research, but simulating even the simplest cell at that resolution is so computationally demanding that it's infeasible even for the world's best supercomputers. But the aggregate computing power of the Bitcoin network is orders of magnitudes larger than any supercomputer, and might be able to run such a model in a reasonable amount of time. By adopting that model for in silico cells, a crucial part of medical research is essentially collectivized, and as part of our design, so too are the results of that research. The project bears similarity to Folding@Home and its crypto-based derivatives (e.g. FoldingCoin), but as far as I know none of these projects explicitly distribute ownership of the research that results from the network. There were also some design details that we didn't have time to hash out, and we left open a big question of computational verifiability: given a simulation update from a node, how can you be certain that they actually computed that value rather than returned some random value? (Golem has this problem too, the difficulty of which is discussed a bit here).

This time around, rather than a project about medical research abstractly, we focused specifically on the pharmaceutical industry, the 1.1 trillion dollar business lying at the nexus of intellectual property law, predatory business practices, and the devaluing of human life.

The pharmaceutical industry

“Is curing patients a sustainable business model?” Goldman Sachs analysts ask
“Is curing patients a sustainable business model?” Goldman Sachs analysts ask

(For background I'm going to lean heavily on the "Pill of Sale" episode of the Ashes Ashes podcast which goes into more detail about the pharmaceutical industry — definitely worth a listen.)

Most Americans are familiar with exorbitantly-priced drugs — if not directly than via one of the many horrifying stories of people crowdfunding their continued existence or flying elsewhere to access more reasonable prices. A hepatitis C cure from Gilead, Solvadi, costs $84,000 for a 12-week course and is the subject of a recent Goldman Sachs report. The report describes cures as effective as Solvadi (up to 97%) as bad for business since you cure yourself out of a market. Even something as common insulin can cost a significant portion of income — to the point where people die from needing to ration it.

This hostile environment is thinly justified with rhetoric around drug development costs and enforced through the patent law system, all under the implicit, sometimes explicit, assumption that it is necessary for drug companies to make a profit on their drugs. Patents provide exclusive rights for a company to sell a particular drug; this temporary monopoly essentially gives them carte blanche to set whatever price they want so that they recoup the drug development costs, so the story goes. These patents last 20 years and can basically be extended by "exclusivity" periods which add up to another 7 years. A drug may take 10-15 years to develop, leaving a window of at least 5 years of exclusive rights to produce and sell it. "Orphan drugs", drugs that treat rare conditions, may have longer monopolies to compensate for the smaller market size. After this period generics are permitted to enter the market, which drives the cost down, but there are all sorts of tricks available that can prolong this protection period even further, a practice called "evergreening". For example, slightly modifying how the drug is delivered (e.g. by tablet or capsule) can be enough for it to essentially be re-patented.

(It's worth noting that prices can be high even for generics. For example, epinephrine — commonly known as an EpiPen, essential for severe allergic reactions — can be bought for about 0.10-0.95USD outside the US, whereas generics in the US can cost about $70.)

Drug development pipeline. From: Pharmaceutical Research and Manufacturers of America, Drug Discovery and Development: Understanding the R&D Process,
Drug development pipeline. From: Pharmaceutical Research and Manufacturers of America, Drug Discovery and Development: Understanding the R&D Process,

Drug development is expensive, averaging at over $2.5 billion per drug, and that's only counting for those that gain FDA approval. However, these exclusivity rights are not merely used to recapture R&D costs, as is often said, but instead to flagrantly gouge prices such that the pharmaceutical industry is tied with banking for the largest profit margins of any industry (as high as 43% in the case of Pfizer).

The narrative around high drug development costs also takes for granted that pharmaceutical companies are the ones bearing all of these costs. A considerable amount of the basic research that is foundational to drug development is funded publicly; the linked study found that public funding contributed to every drug that received FDA approval from 2010-2016. The amount of funding is estimated to be over $100 billion.

It used to be that inventions resulting from federal funding remained under federal ownership, but the 1980 Bayh–Dole Act offered businesses and other institutions the option to claim private ownership. The result is the public "paying twice" for these drugs. The Act does preserve "march-in rights" for the government, allowing the government to circumvent the patent and assign licenses independently if the invention is not made "available to the public on reasonable terms", but as of now these rights have never been exercised. In 2016 there was an unsuccessful attempt to use these march-in rights to lower the price of a prostate cancer drug called Xtandi, priced at $129,000/year.

All of this isn't to say that the work of the pharmaceutical industry isn't valuable; drugs are a necessary part of so many peoples' lives. I recently started using sumatriptan to deal with debilitating migraines, and am hugely grateful it exists (and is not ridiculously expensive). It's because pharmaceuticals are such a critical part to life that their development and distribution should not be dictated the values that currently shape it.

One particularly egregious example of this mess is the nightmare scenario of Purdue Pharmaceuticals, owned by the Sackler family (who are also prolific patrons of the arts), producers of OxyContin (accounting for over 80% of their sales last year), basically responsible for the ongoing opioid crisis (affecting at least 2.1 million Americans directly, and many more collaterally), and recently granted a patent for a drug that treats opioid addiction. The patented treatment is a small modification of an existing generic.

The day before our 7x7 presentation a story broke in the Guardian: "Sackler family members face mass litigation and criminal investigations over opioids crisis".

Computational drug discovery

One reason drug development is so difficult is that the space of possible drug compounds is extremely large, estimated to be between 1060 and 1063 compounds. For comparison, there are an estimated 1022 to 1024 stars in the entire universe, and according to this estimate about 1049-1050 atoms making up our entire world.

PubChem's chemical space, from "Exploring Chemical Space for Drug Discovery Using the Chemical Universe Database"
PubChem's chemical space, from "Exploring Chemical Space for Drug Discovery Using the Chemical Universe Database"

Drug development is in large part a search problem, looking to find useful compounds within this massive space. A brute-force search is impossible; even if it took only a couple seconds to examine each possible compound you'd see several deaths of our sun (a lifespan of about 10 billion years) before fully exploring that space.

More effective techniques for searching this space include slightly modifying existing drugs for different therapeutic applications ("me-too" compounds) and literally looking at plants and indigenous medical traditions for leads (this general practice is called "bioprospecting" and this particularly colonialist form is called "biopiracy").

Of course with the proliferation of machine learning there is a big interest in searching this space computationally. Two main categories are virtual screening (looking through known compounds for ones that look promising) and molecular generation (generating completely new compounds that look promising). We focused on molecular generation for reasons described below.

A primary goal for is to publicize this work in computational drug discovery and also help researchers use these generated compounds as potential leads for new beneficial drugs. With our system, which is also open source, independent and institutional researchers alike can access automated drug discovery technology and hopefully accelerate the drug development process.

Prior art and public discovery of drugs

(For this section we spoke with a patent lawyer who requested that we note that they are not representing us.)

One crucial criteria for a patent is that the invention must be novel; that is, the invention cannot have already been known to the public. An existing publicly-known instance of an invention is called "prior art" and can invalidate a patent claim. However, sufficient variations to an invention may qualify it as original enough to be patentable (this is the idea behind evergreening, described above).

If a drug is discovered and made public prior to a patent claim on it, it would function as prior art and make that compound un-patentable in its current form. If we were able to generate new molecules that could function as useful drugs, and make public those new molecules, then perhaps we can prevent companies from patenting them and maintaining a temporary monopoly on their distribution.

This is the second goal of develop an automated drug discovery system to find and publish useful drugs so they cannot be patented.

Additional efforts

Other efforts to the address problems with pharmaceutical industry can be found in initiatives like Medicare for All and the proposed Prescription Drug Price Relief Act, and the organizing happening around those. The issues with the pharmaceutical industry are just one piece of a more general hostility in American healthcare.

There is also a burgeoning DIY medicine movement which aim to build alternatives to industrialized medicine, providing autonomy, access, and reliability where those are normally withheld. For example, the artist Ryan Hammond is working on genetically modifying tobacco plants to produce estrogen and testosterone, and the Four Thieves Vinegar Collective (discussed in the Ashes Ashes "Pill of Sale" episode) provides instructions for a DIY EpiPen and a DIY lab ("MicroLab") for synthesizing various pharmaceuticals, including Naloxone and Solvadi.

That's it for the background of the project. The following section describes how the system works in more detail.

The project code is available here.

How it works

The complete system involves three components:

  1. A molecular generation model, using a version of the graph variational autoencoder ("JTNN-VAE") described in [2], modified to be conditional ("JTNN-CVAE"). This generates new compounds given a receptor and action, e.g. a nociceptin receptor agonist.
  2. An ATC code prediction model. The Anatomical Therapeutic Chemical (ATC) classification system categorizes compounds based on their therapeutic effects. We use this to estimate what a generated compound might treat.
  3. A retrosynthesis planner. Retrosynthesic analysis is the process of coming up with a plan to synthesis some target compound from an inventory of base compounds (e.g. compounds you can purchase directly from a supplier). This is necessary to meet the enablement requirement of prior art; that is, it's not enough to come up with a new compound, you also need to sufficiently demonstrate how it could be synthesized.

To train these various models we relied on a number of public data sources, including PubChem, UniProtKB, STITCH, BindingDB, ChEMBL, and DrugBank.

Common chemical compound representations, from Prediction methods and databases within chemoinfromatics: emphasis on drugs and drug candidates.
Common chemical compound representations, from Prediction methods and databases within chemoinfromatics: emphasis on drugs and drug candidates.

Chemical compounds can be represented in a number of ways, e.g. as a 2D structural diagram or a 3D model. One of the most portable formats is SMILES, which represents a molecule as an ASCII string, and is what we use throughout the project.


For training the JTNN-CVAE model we needed to cluster the compounds in a meaningful way. At first we didn't look at receptors and actions but rather tried to leverage the vast published chemical research literature (in PubMed) and USPTO patents.

For the first attempt we tried learning word2vec embeddings from PubMed article titles and abstracts, then representing documents using TF-IDF as described in the WISDM algorithm, and finally clustering using DBSCAN or OPTICS. This ended up being way too slow, memory intensive, and limited in what clustering algorithms we could try.

The second attempt involved generating a compound graph such that an edge exists between compound A and B if they appear in an article or patent together. So instead of linking compounds based on the content of the articles they're mentioned in, they're linked solely on the virtue of being mentioned together in an article, under the assumption that this indicates some meaningful similarity. Then we ran a label propagation community detection algorithm to identify clusters within the graph. The graph was fairly sparse however and in the end looking at the connected components seemed to be enough. There were still some limitations with speed and memory that led us to abandon that approach.

Finally we decided to cluster based on receptors compounds were known to interact with. This reduced the amount of compounds we were able to look at (since the compounds for which receptor interactions are known are much less than the total of all known compounds), but the data was richer and more explicit than co-mentions in text documents. A drug's effects are determined by the receptor it targets and how it interacts with that receptor (does it activate it, does it block it, etc). For instance, OxyContin is a mu-type and kappa-type opioid receptor agonist (it activates them), and Naloxone (used to treat opioid overdose) is a mu-type and kappa-type opioid receptor antagonist (it blocks them).

G-Protein Coupled Receptor, from Random42
G-Protein Coupled Receptor, from Random42

The ChEMBL data has information about both receptors and the type of interaction (agonist, antagonist, etc), whereas BindingDB has information about only the receptors but for more compounds. So we used a two-pass approach: for the first pass, we create initial clusters based on receptor-action types and on the second-pass we augment these clusters with the BindingDB compounds, assigning them to the cluster that matches their target receptor and has the highest fingerprint similarity to the cluster's current members. This resulted in 461 receptor-action clusters.

Molecular generation model

The JTNN-VAE model described in [2] is a variational autoencoder that handles molecular graphs. A variational autoencoder is a generative model that learns how to compress ("encode") data in such a way that it can be reliable decompressed ("decoded"). It accomplishes this by learning an underlying probability distribution that describes the data. Once the model has learned this distribution you can sample it to generate new data that looks like the old data. This post provides an overview on variational autoencoders.

We modified the model to be conditional, allowing us to sample from the learned probability distribution conditioned on the cluster (the receptor-action) we want to generate new compounds for.

ATC code prediction model

The ATC code prediction model is a straightforward multiclass neural network. Though ATC code prediction is technically a multilabel problem (a compound may have more than one ATC code), most compounds had only one code, so we treated it as a multiclass problem. ATC codes have 5 levels, from low detail to high detail; we predict level 3 codes ("pharmacological/therapeutic/chemical subgroup"), with one class for each level 3 code. We use 2048-bit Morgan fingerprints as representations for the compounds and achieved about 80% accuracy with this naive approach — not ideal, but fine for our purposes and time constraints.

Retrosynthesis planner

Here's where it came down to the wire. We attempted to implement the model ("3N-MCTS") described in [7], which involves Monte Carlo Tree Search (MCTS) and three policy networks. The policy networks predict what reaction rules might apply to a given compound. The paper's model is trained on Reaxys data, which is way too expensive for us, so we used a smaller dataset extracted from USPTO patents (from [26]). Our implementation is basically complete but we didn't have enough time to train the models.

In an 11th-hour Hail Mary we used MIT's ASKCOS system which got us mostly partial synthesis plans. At some point we should revisit the 3N-MCTS system to see if we can get that working.

Sampling and filtering

Once all the models were ready we sampled 100 new compounds for each of the 461 receptor-action clusters. Then we filtered down to valid compounds (with no charge) and to those not present in PubChem's collection of 96 million compounds. We ended up whittling the set down to about 15,000 new compounds for which we then predicted ATC codes and generated synthesis plans.

Future work

Time was fairly tight for the project so we didn't get to tune or train the system as much as we wanted to. And it would have been nice to try to experiment with more substantial modifications of the models we used. But for me this project was a wonderful learning experience and renewed an interest in chemistry. I want to spend some more time in this area, especially in materials science because of its relevance to lower-impact technologies, such as this cooling material.


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Culture: A Social Network Simulator

This is a proposal for Culture, a social network simulator designed and developed to teach students about bot development.

This proposal was originally developed for a class on "news bots" I was scheduled to teach in the fall of 2017 (I ended up having a conflict and was unable to teach it). I wanted students to not only explore the impact of bots from a theory perspective, but also engage hands-on to see just how radically influential these bots are on social media platforms.

And not only bots. Ideally students would take on the role of other actors in social media ecosystems, such as a "traditional" media publication, or as an advertiser, or as a political candidate, or as a influencer, or even as the platform itself, making decisions around aspects such as the newsfeed algorithm.

Unfortunately, there are a number of challenges that make hands-on experience infeasible with live social networks:

  • Ethical concerns. For example, many bots are meant to deceive and manipulate, and we'd be working with real user data.
  • Issues of access. For example, rate-limiting and limited access to data. For privacy reasons APIs generally don't provide sensitive user data to developers, though some such data may be provided to advertisers. And of course, with live social networks there isn't a way for students to change the newsfeed algorithms for the entire network.
  • Limits of reality. For example, a student can't magically become an influencer on Twitter, but in a simulated setting, they can.

There are also some technical obstacles, namely that students taking the class weren't required to have any programming background and I didn't want to spend too much time on introductory programming lessons. Even if students were fairly experienced in programming, working with bots has a lot of advanced challenges, such as dealing with natural language. A simulated social network can be simplified so that these problems are easier to deal with.

This proposal doesn't really have a strong advertising component. After speaking with Irwin Chen about it, I realized it's a pretty big omission. So an updated proposal will include all of that: selling ads, ad targeting, ad exchanges, etc. It's not an area I know well, so I'd have to speak with some people and do some research before sketching that out.


Culture will be an agent-based simulation of a simple social network modeled off of Twitter. As such, the simulation will consist of the following (each part is elaborated further below):

  • users communicate in a rudimentary language
  • users have different personalities
  • each user will have a feed of messages from people they follow and include promoted/ad messages
    • here students can potentially design their own news feed algorithms and see how that affects individual/public opinion
  • users can message, post media, block, be blocked, be banned, follow, unfollow
  • messages and media influence users
  • the network responds to and affects outside events


So much of our exposure to and understanding of the world beyond our immediate experience is mediated by social networks, which is to say by newsfeed algorithms and other individual users of these networks. Students should develop a stronger literacy in these dynamics if they are to adequately navigate this information ecology.

This literacy is best developed by direct interaction with these social networks, such as Twitter or Facebook, rather than through theory alone. However, working directly with these networks may be impractical in that they are massive, closed-source, and limited in access. For instance, due to API limits it is impossible to survey or conduct analyses of the entire population of the network, or to examine in detail its inner operations.

Furthermore, there is no room for counterfactual speculation in these existing social networks. For example, we can't intervene and change the behaviors of all users and see how information propagation changes as a result. This limits the pedagogical value of working directly with, for example, Twitter or Facebook.

A simulated social network addresses these concerns. It can be designed to model the dynamics of its real counterparts, it can be entirely open in that students have access to all the network's data, and its parameters can be tweaked to see how information propagation evolves under different circumstances. Students can develop bots on this network without worrying about API limits, spam protection, and so on. In contrast to the black-box nature of a real social network, a simulated social network functions more like a sandbox.


The simulated agents are individual users of the social network. They are randomly generated to have particular personalities and interests (see below). Their generation is part of the simulation's initialization. Students do not directly interact with these agents, but can indirectly interact with them via, for example, ads and bots they create (see below).


Dealing with natural language is difficult even for experienced developers and advanced researchers in the topic. Broadly, the problem of natural language in the context of bots can described in two parts: understanding and generation. Both are very difficult and beyond the scope of the courses that this simulation is designed for, which includes introductory classes.

To avoid dealing with natural language, the simulation will consist of a very basic grammar and a relatively small vocabulary which can be easily expanded as needed. Because of its relatively simplicity, the same natural language processing techniques that are currently used for "real" languages can also be applied, but with greater success, and better yet, simpler heuristics will go a longer way. Thus students will not need to have a deep understanding of, for example, word vectors or TF-IDF, but may develop their own simpler techniques that will still be effective.

This simpler language will consist of verbs, nouns, and modifiers (adjectives and adverbs) (collectively, "terms"). Because the courses are assumed to be taught in English, this language will be reflective of English.

These terms are combined into formal propositional statements, e.g. single-payer-healthcare + country -> < freedom, which expresses the opinion that implementing single payer health care in this country will cause (->) a loss (<) of freedom. (This is just a sketch of the syntax; it's subject to change).

This is a bit limiting; there is no room for poetics, for instance, but will provide a strong starting point that can be expanded on later.

The design of this language will involve developing a network of terms (i.e. defining term associations), such that terms represent mixtures of other terms and values in the simulation (e.g. individuality/collectivism, see "Personalities" below). This term association network is opaque to the students; they do not get to see what these terms mean to the agents in the simulation. As with the real world, they must use algorithms or their own intuition from observing the network to determine what language best communicates their messages.

For example: the term "car" may be connected to the terms "individuality" and "freedom" to establish that the term "car" symbolically evokes these two ideas. We could then imagine ads for "cars" appeal more to agents with personalities that align more with those concepts relative to agents who, for example, align more with "collectivity" and "freedom".

Terms also have sentiment valences, e.g. "bad" may have a valence of -0.5 to express a negative opinion, whereas "terrible" may have a stronger valence of -0.8, and so on.

Ideally, this term association network is not objective but rather subjective; i.e. differs depending on the particular agent. For example, the term "freedom" may be associated with different values for one agent than for another. However, it is likely that this will be computationally infeasible (though some kind of heuristics could be developed to simplify it).

This term association network also changes over time as terms are used in slightly different contexts. This provides a way for the meaning of terms to change or be entirely inverted, e.g. a negative term being co-opted as a positive identifying term for a group.

The language is the part of the simulation that will require most care in designing - it needs to represent important aspects of how language is used in social networks (e.g. to express opinion/judgement, to harass/abuse, to make propositional statements, etc).


Simulated agents will have could loosely be described as "personalities"; that is, a set of parameters that determines how the agent interacts with others (e.g. aggressiveness/friendliness, within-bubble/outside-bubble, etc) and what their values are (e.g. conservative/progressive, individualist/collectivist, etc). These personalities will be generated randomly, via a Bayes Net (or some similar probabilistic model) that will be editable in some way. A model like a Bayes Net lets us describe assumed relationships between values (e.g. more collectivist agents are more likely to be friendly).

These personalities also determine who agents tend to interact with (under principles of homophily, i.e. like attracts like) and also what kind of messaging resonates with them (e.g. messages about rugged individuality will resonate more with individualist agents).


"Messages" are the equivalent of Twitter's tweets. Agents compose their own messages based on their personalities and who they are interacting with. Messages may affect an agent's mood and also their personality (see below).


Text is not the only important part of a social network - memes and other media (news stories, videos, etc) form a crucial part of their information flow.


Agents' states include their personalities, in addition to other attributes like mood and use frequency (how often they visit the social network) and post frequency (how often they post messages). Mood may affect, for example, how agents interact with other agents (e.g. with more or less hostility). This can be used to model emotion contagion.


Based on who they interact with and what other messaging they are exposed to (e.g. targeted ads), the personalities (traits/opinions) of an agent may shift over time. Various social phenomena, e.g. bipolarization, can be modeled here.


Social networks are not closed systems; they do not exist in isolation. The "outside" world affects what goes on in network, just as what goes in the network can spill out and effect the outside world.

Part of the simulation will support external events (also simulated) that affect and can be affected by the social network, such as an election. The outside event(s) affect what are popular topics (i.e. topics that are relevant and agents are more likely to talk about and respond to) and they can be defined to have some relationship to the shape of discourse in the network.

Social Network

In this section, "social network" is used not to refer to the platform itself, but to the actual network of relationships between users (expressed by "following" relationships). Some users may be highly connected (many followers), and students may, for example, as part of their strategy (whether for ads or opinion influence) try to target these opinion leaders.


It will likely be too computationally taxing to display all activity on the social network, but students will have access to various views that provide summaries (i.e. mean sentiment towards some topic, number of users talking about a topic, etc). Ideally an API can be provided like a real social network, so that students can build their own visualizations as part of their bot development process, but this may be limited by the size of the simulation.


A simple API will be provided for students to develop their own bots that interact with this network. These bots can follow, be followed, message, etc like agents can and will be the primary way students interact with the social network.

What distinguishes bots from simulated agents is that bots are designed and controlled by students, whereas the simulated agents represent "real" users of the network.

Learning Objectives

The network functions as a simplified social landscape for students to understand how ads, bots, and news feed algorithms affect opinion, trends, and discussion on a social network, and how that links up with broader spheres of discourse outside of the network. Some students may, for example, design bots that influence opinion in a certain direction, while others may design bots to influence opinion in a different direction, while still others may design bots that root out these interfering bots. Depending on how the network is designed, some students can be the managers of the social network platform.

The goal is for students to develop a comprehensive mental model about the dynamics of social media and communication in the internet age, to peek "behind the curtain" and develop a critical perspective when using social media and reading the news (i.e. develop social media literacy).


In theory this simulated social network can be extended with features that could be present on any social network, such as anonymous accounts, different kinds of blocking and muting functionality, and so on. Thus it can also be a place where students can experiment with new features to see how that affects dynamics on the network.


Ideally the simulator accommodates students who are comfortable with programming and those who aren't.

For students who aren't, bot templates could be provided which require little to no programming experience, or another layer can be developed where they "purchase" different bot, marketing, and so on services that run automatically.

If there are multiple classes going on, they can all work from the same simulation and take on different roles. If one class is focused on advertising, they can take on roles of the advertising ecosystem, while in another class perhaps they collectively take on the role of the platform. The potential for cross-class interactivity is exciting.

7x7 Cutting Room Floor

I was fortunate enough to participate in this year's edition of Rhizome's Seven on Seven with Sean Raspet. The event pairs an artist and a technologist and gives some limited time for the pair to come up with and implement a concept or project. In previous editions pairs only had a day or so; this time we had about a month.

It was enough time to churn through several ideas that never made it to the final presentation. We landed on producing a white paper proposing leveraging blockchain-based distributed computing to collectively simulate a complete human cell at the atomic level, starting with something relatively simple like a red blood cell. A human cell might have hundreds of trillions of atoms and so simulating one at the atomic resolution is basically infeasible with existing computational resources. But it is more feasible now than it was maybe a decade ago.

Through our research we came across some staggering statistics about the computing power of the Bitcoin network, namely that it is estimated to have an aggregate computing power of 80.7 zettaFLOPS (80.7 million petaFLOPS) as of May 2018. The world's reigning supercomputer, the Sunway TaihuLight, has a theoretical peak of 125 petaFLOPS. The Folding@Home network, which enables people to donate spare computing power for protein folding simulations, had an aggregate power of about 100 petaFLOPS in January 2018. Not bad for a volunteer distributed network, but still far off from the Bitcoin network. There are more details in the white paper, but those numbers stuck out.

Anyways, we went through a few ideas before we landed on this white paper. Our first focus was on the phosphorus commodity market in relationship to "peak phosphorus". This was something Sean had been researching for some time now, and for the past few months I've been poking around the agri-tech scene, so I was naturally drawn to it as a topic. The gist is that phosphorus is a mineral crucial to agriculture, a key component in fertilizers (along with nitrogen and potassium; the history of nitrogen fertilizer is very interesting and troubling one), and is basically a non-renewable resource (some can be recovered from waste but I'm not sure what percentage of it is recoverable). At some point in the relatively near future phosphorus extraction may become too expensive or difficult and that could lead to some serious food security crises. So we were thinking of various ways to represent this issue. Here are a few ideas we played around with.

Global phosphorus simulation

Phosphorus, like any resource-extractive industry, is global. We wanted to be able to convey geopolitical issues like Morocco's occupation of Western Sahara, which is where Morocco mines its phosphorus. The most straightforward way to do something like that is a 4X-style global simulation, so we played around with that first.

I designed a little framework for laying out a hex-based map (similar to the cartog library I created for my Simulation & Cybernetics class, but I wanted to support 3D):

3D hex-based maps
3D hex-based maps

That's about as far as we got in terms of implementation. But the general idea was that we'd model the dynamics of the global phosphorus market, with some shocks and random events, and projections of changes in relevant indicators like growth rates, meat consumption rates, and so on. And somehow you'd see these effects on this map and through changes in the price of commodity phosphorus.

I didn't want this hex map to be the only "output" of the simulation. We wanted to show that the macro-level dynamics of the phosphorus market are intimately connected to the health of individual plants, and so I wanted to setup an automated growing system as a more material visualization. The system would be hydroponic or aeroponic, with a phosphorus nutrient pump that releases more or less phosphorus depending on its simulated price. As peak phosphorus approaches, the plant's health starts to deteriorate as it manifests symptoms of phosphorus deficiency. There were a few issues here, namely that 1) it's a pretty big task to set up such a growing system, and 2) the changes in the plant's health would happen over long time scales relative to the simulation (e.g. one simulation year might run in one real minute, and the impacts on the plant's health might not be visible for a few real days).

Phosphorus deficiency in corn
Phosphorus deficiency in corn

A build on this idea we considered is that we'd reserve some set amount of funds for the plant, and it would actually have to "purchase" phosphorus from the nutrient reservoir on its own.

Commodity traders vs food consumers

For awhile I've wanted to make an asymmetric game which consists of two separate games that are at first glance unrelated. For example, on one side of the room is a relatively innocuous-looking life simulator game where you have to e.g. buy a house and care for your family. On the other side of the room is a stock market game where you just try to earn the highest return on your investments. What isn't apparent at first is that the actions of the player in the stock market game directly affect how difficult the life-simulator game is, for example, by triggering financial crises or affecting house prices.

We briefly considered doing something along these lines. The idea was that when we presented, we'd direct audience members to a website where they could join our phosphorus game. Some audience members would be redirected to the "commodity trader" version of the game, while others would instead be redirected to the "food consumer" version.

The commodity trader game is basically same as the stock market game, except just for phosphorus trading.

Commodity trader interface
Commodity trader interface

The food consumer game is built around a "basket", like a simplified version of a consumer price index focused on products especially affected by phosphorus prices. As a player you'd have some nutritional requirements to meet or some other purchasing obligations and some weekly budget with which to buy food. We didn't really get far enough to thoroughly think through the mechanics.

Food consumer interface
Food consumer interface

I did have a really fun time modeling the food:

Food models
Food models

Plant care Tamagotchi

Riffing off the plant-as-visualization idea, we also toyed around with the idea of some kind of plant-tamagotchi. You'd have to manage its water and phosphorus needs by doing some sort of trading or other gameplay. I can't really remember how far we got with the design.

Plant care
Plant care

I did enjoy making this wilting animation though:

Plant wilting
Plant wilting

Physics-based food thing

I honestly can't remember what the concept was for this. The most I can recall is that we discussed a system where you could rapidly click on some food or raw material objects to create derivative objects (such as beef and milk from a cow) and that somehow we'd connect that to the relative use of phosphorus in these products. For example, a cow requires a lot of feed which requires a lot of phosphorus, which results in a less efficient phosphorus-to-calorie ratio than if you had just eaten the feed grains yourself. I think I was really just excited about making something physics-based.

Picking up objects
Picking up objects
Tapping on objects for derivatives
Tapping on objects for derivatives
Bouncing around
Bouncing around

I'll definitely use this again for a different project.

Scripts I Have Known And Loved

I've been using Linux as my main driver (Ubuntu 14.04, recently and catastrophically upgraded to 16.04, with no desktop environment; I'm using bspwm as my window manager) for about two years now. It's been challenging and frustrating, but ultimately rewarding — the granular control is totally worth it.

Over these two years I've gradually accumulated a series of Bash and Python scripts to help me work quickly and smoothly. They generally operate by two principles: accessible from anywhere and usable with as few keystrokes as possible.

All the scripts are available in my dotfiles repo in the bin folder.

Screenshots and recordings

A few of the scripts are devoted to taking screenshots (shot) or screen recordings (rec). This is a basic feature in OSX (and many Linux desktop environments, afaik), but something I had to implement manually for my system. The benefit is being able to customize the functionality quite a bit. For example, the rec script will automatically convert the screen recording into an optimized gif (using another script, vid2gif).

The shot script lets me directly copy the image or the path to the screenshot immediately after it's taken, but sometimes I need to refer to an old screenshot. It's a pain to navigate to the screenshot folder and find the one I'm looking for, so I have another script, shots, which lets me browse and search through my screenshots and screengifs with dmenu (which is a menu that's basically accessible from anywhere).


Passwords and security

Entering passwords and managing sensitive information is often a really inconvenient process, but some scripts make it easier.

I use KeePassX to manage my passwords, which means when I want to enter a password, I have to open up KeePassX, unlock the database, search for the password, and then copy and paste it into the password input.

This is a lot of steps, but my keepass script does all this in much fewer keystrokes.

I open it super+p, enter my master password, directly search for my password, and select it. Then it pastes the password into the input form automatically.

It can also create and save new passwords as needed.


I use 2FA on sites that support it, which means there's another step after entering a password - opening up the authenticator to get the auth code. I have another script, 2fa, which I open with super+a, that copies the appropriate auth code into my clipboard so I can paste it in straight away.

These two scripts make logging in and good account security way easier to manage.

For local data that I want to encrypt, I have a script called crypt that lets me easily encrypt/decrypt individual files or directories with my GPG key. I use this script in another script, vault, which makes it easy to encrypt/decrypt a particular directory (~/docs/vault) of sensitive information.

Finally, I have a lock script for when I'm away from my computer that pixelates the screen contents and requires my password to unlock (this was snagged from r/unixporn).


Working with hubble

My main driver is a relatively low-powered chromebook (an Acer C720), so for heavy processing I have a beefy personal server ("hubble") that I access remotely. It's not publicly accessible - as in, it's not a box provided by a service like Digital Ocean but a literal computer under my desk. This introduces some challenges in reliably connecting to it from anywhere, so I have a few scripts to help out with that.

hubble can consume quite a bit of power so I don't like to leave it running when it's not in use. It's easy enough to shut off a server remotely (shutdown now) but turning it on remotely is trickier.

There's a really useful program called wakeonlan that lets you send a special packet to a network interface (specifying its MAC address) that will tell its machine to boot up. However, you still need a computer running on the same network to send that packet from.

I keep a Banana Pi running at all times on that network. Its power consumption is much lower so I don't feel as bad having it run all the time. When I need to access hubble, I ssh into the Pi and then run wakeonlan to boot it up.

This Pi isn't publicly accessible either - there's no public IP I can ssh directly into. Fortunately, using the script tunnel, I can create an ssh tunnel between the Pi and my hosting server (where this website and my other personal sites are kept), which does have a public IP, such that my hosting server acts as a bridge that the Pi piggybacks off of.

Finally, sometimes I'll run a web service on hubble but want to access it through my laptop's browser. I can use portfwd to connect a local laptop port to one of hubble's ports, so that my laptop treats it as its own. It makes doing web development on hubble way easier.


I have several other scripts that do little things here and there. Some highlights:

  • q: quickly searches my file system, with previews for images.
  • sms: lets me send an arbitrary notification over Signal, so I can, for example, run a long-running job and get texted when it's finished: ./slow_script && sms "done!" || sms "failed!". This also accepts attachments!
  • twitch: lets me immediately start streaming to Twitch.
  • caffeine: prevents the computer from falling asleep. I have it bound to super+c with a eye indicator in my bar.
  • phonesync: remotely sync photos from my phone to my laptop and media from my laptop to my phone (they must be on the same LAN though).
  • emo: emoji support on Linux is still not very good; this script lets me search for emoji by name to paste into an input. Still thinking of a better solution for this...
  • bkup: this isn't in my dotfiles repo but I use it quite a bit — but it lets me specify a backup system in YAML (example) that is run with bkup <backup name>.
  • office: unfortunately this repo is not yet public (need to clean out some sensitive info) but this is a suite of scripts that automates a lot of freelance paperwork-ish stuff that I used to do manually in InDesign or Illustrator:
    • generate invoices from a YAML file
    • generate contracts from a YAML file
    • generate a prefilled W9
caffeine indicator
caffeine indicator

Conspiracy Generator

I recently wrote a conspiracy-generating bot for The New Inquiry's Conspiracy issue. The basic premise is that far-fetched conspiracy theories emerge from the human tendency for apophenia - finding meaning in random patterns - and that the problematic algorithm-level aspects of machine learning are instances of a similar phenomenon. The bot leverages how computer models can misidentify faces and objects as similar and presents these perceptual missteps as significant discoveries, encouraging humans to read additional layers of absent meaning.

The bot consists of the following core components:

In this post I'll give a high-level explanation for the basic structure of the bot. If you're interested in details, the full source code is available here.

Sourcing images, the material of conspiracy

For this bot the ideal conspiracy is one that connects seemingly unrelated and distance events, people, and so on not only to each other but across time. If that connection is made to something relevant to the present, all the better. The bot's source material then should combine obscure images with a lot of breadth (encompassing paintings, personal photos, technical drawings, and the like) with immediately recognizable ones, such as those from recent news stories.

Wikimedia Commons is perfect for the former. This is where all of Wikipedia's (and other Wiki sites') images are hosted, so it captures the massive variety of all those platforms. Wikimedia regularly makes full database dumps available, including one of all of the Commons' image links (commonswiki-latest-image.sql.gz). Included in the repo is a script that parses these links out of the SQL. The full set of images is massive and too large to fit on typical commodity hard drives, so the program provides a way to download a sample of those images. As each image is downloaded, the program runs object recognition (using YOLO; "people" are included as objects) and face detection (using dlib), saving the bounding boxes and crops of any detected entities so that they are easily retrieved later.

Extracted faces and objects
Extracted faces and objects

For recent news images the bot uses reality, a simple system that polls several RSS feeds and saves new articles along with their main images and extracted named entities (peoples, places, organizations, and so on). When new articles are retrieved, reality updates a FIFO queue that the bot listens to. On new articles, the bot runs object recognition and face detection and adds that data to its source material, expanding its conspiratorial repertoire.

Each of these sources are sampled from separately so that every generated conspiracy includes images from both.

At time of writing, the bot has about 60,000 images to choose from.

Generating conspiracies

To generate a conspiracy, 650 images are sampled and their entities (faces and objects) are retrieved. The program establishes links between these entities based on similarity metrics; if two entities are similar enough, they are considered related and included in the conspiracy.

Face and object distance matrices
Face and object distance matrices

The FaceNet model used to compute face similarity. For objects, a very naive approach is used: the perceptual hashes of object crops are directly compared (perceptual hashes are a way of "fingerprinting" images such that images that look similar will produce similar hashes).

Links generated by similarity
Links generated by similarity

Because reality saves a news article's text along with its image, we can also search through that text and its extract entities to pull out text-based conspiracy material. The program starts by looking for a few simple patterns in the text, e.g. ENTITY_A is ... ENTITY_B, which would match something like Trump is mad at Comey. If any matches are found, a screenshot of the article's page is generated. Then optical character recognition (OCR) is run on the screenshot to locate one of those extracted phrases. If one is found, a crop of it is saved to be included in the final output.

This network of entity relationships forms the conspiracy. The rest of the program is focused on presentation.

Image layout and annotation

Once relationships between faces and objects are established, the next challenge is to present the implicated images in a convincingly conspiratorial way. This problem breaks down into two parts: image 1) layout and 2) annotation.

1) Layout

Layout is tricky because it's a bin packing problem, which is NP-hard, but fortunately there exists implementations of good solutions, such as rectpack, which is used here. There is no guarantee that all of the selected images will fit, so there is some additional processing to ensure that the images that do get included have a maximal amount of connectivity between them.

The generated network of entity relationships forms a graph of the selected images. Two images have an edge between them if at least one pair of their entities are linked. We start with the image that has the highest degree (i.e. the image that is connected to the most other images). We look at what images its connected to and pick the image with the highest degree out of those, and repeat. The result is a sequence of connected images, descending by degree.

Ordering images based on links
Ordering images based on links

Here's where some style is added. Images are not placed exactly as prescribed by the bin packing algorithm; there is some "shakiness" where they are placed with some margin of error. This gives the layout a rushed, haphazard look, enhancing the conspiracy vibe.

2) Annotation

A conspiracy's entities need to be highlighted and the links between them need to be drawn. While Pillow, Python's de facto image processing library, provides ways to draw ellipses and lines, they are too neat and precise. So the program includes several annotation methods to nervously encircle these entities, occasionally scrawl arrows pointing to them, and hastily link them.

These were fun to design. The ellipse drawing method uses an ellipse function where the shape parameters a, b are the width and height of the entity's bounding box. The function is rotated to a random angle (within constraints) and then the ellipse is drawn point by point, with smooth noise added to give it an organic hand-drawn appearance. There are additional parameters for thickness and how many times the ellipse should be looped.

"Hand-drawn" annotations
"Hand-drawn" annotations

This part of the program also includes computer text, which are randomly sampled from a set of conspiracy clichés, and also writes entity ids on the images.

Text annotations
Text annotations

Further manipulation

Images are randomly "mangled", i.e. scaled down then back up, unsharpened, contrast-adjusted, then JPEG-crushed, giving them the worn look of an image that has been circulating the internet for ages, degraded by repeat encodings.

Image mangling
Image mangling

Assembling the image

Once all the images are prepared, placed, and annotated, the final image is saved and added to the webpage.

There you have it - an algorithm for conspiracies.