AI in Drug Discovery Glossary, 2020 Edition

Get Prepared for JPM and 2020's AI in Drug Discovery Conferences

The following was submitted to the Journal of Medicinal Chemistry as a mini-perspective for their special issue on AI in drug discovery. I leave it to the reader to judge whether they were right to reject it forthwith.

The breadth of the field of artificial intelligence (AI) in drug discovery (DD) can be difficult to keep up with for even the most well-read scientists (DTKUWFETMWS)! To ensure that you might be able to count yourself among the best-prepared audience members at this year’s biggest meetings, we (the royal “we”, there’s only one author willing to take credit for this) have prepared this special piece reviewing the top terms you need to know to ensure fluent understanding no matter which parallel talk track you might wander into (you poor soul).

It is widely recognized that if you show up at a conference and admit not knowing one of the terms in Table 1, the organizers may not only ban you from attending in future years, but may in fact ridicule you on stage [1,2]. Don’t let this happen to you — identify terms you don’t recognize, and look up the definitions in the handy-dandy glossary that follows! Then, to reinforce the value of your learning, print out the table and take it with you to your next meeting. Check off a box each time you hear one of the terms, and count up the value of your studying in real time!

NB: while Table 1 includes a central square marked “Free”, we strongly discourage readers from printing out permutations of the table, handing them out to their colleagues, and seeing who can first complete a line of 5 marked squares and shout “BINGO!” in the lecture hall, as that would be uncouth.

Table: The Essential AI in Drug Discovery Cheat Sheet

Data lake End-to-end CRISPR/Cas9 HF/6-31G* State of the art
Virtual screening Graph convolutions Platform solution Embedding Data science
Reinforcement learning Deep learning FREE! Interpretable Digital transformation
Blockchain Knowledge graph Real-world evidence PubChem Bioassay Generative chemistry
AI / Artificial intelligence Robotic lab FAIR Decision support system NaN

Table 1: An index to the most critical terminology for AI-powered drug discovery in 2020. Any resemblances the terms may have to buzzwords, or the table may have to a Bingo card, are purely coincidental.



[1] Haque IS. AI in Drug Discovery Glossary, 2020 Edition.

[2] Muntz, Nelson. “Ha-ha!” Personal communication.

A disclaimer, in case it went over anyone’s head: the views expressed in this post do not reflect the views of my past, present, future, or subjunctive employers on the topic, nor do they really reflect my own. My view is that if we were to be unable to laugh at ourselves, all would be lost.