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Fragment-Based Hit Discovery via Unsupervised Learning of Fragment-Protein Complexes

Try datamol.io - the open source toolkit that simplifies molecular processing and featurization workflows for machine learning scientists working in drug discovery: https://datamol.io/ Never miss another M2D2 talk, add the schedule to your calendar: https://m2d2.io/talks/m2d2/about/ Also consider joining the M2D2 Slack: https://m2d2group.slack.com/join/shar... Title: Fragment-Based Hit Discovery via Unsupervised Learning of Fragment-Protein Complexes Abstract: The process of finding molecules that bind to a target protein is a challenging first step in drug discovery. Crystallographic fragment screening is a strategy based on elucidating binding modes of small polar compounds and then building potency by expanding or merging them. Recent advances in high-throughput crystallography enable screening of large fragment libraries, reading out dense ensembles of fragments spanning the binding site. However, fragments typically have low affinity thus the road to potency is often long and fraught with false starts. Here, we take advantage of high-throughput crystallography to reframe fragment-based hit discovery as a denoising problem – identifying significant pharmacophore distributions from a fragment ensemble amid noise due to weak binders – and employ an unsupervised machine learning method to tackle this problem. Our method screens potential molecules by evaluating whether they recapitulate those fragment-derived pharmacophore distributions. We retrospectively validated our approach on an open science campaign against SARS-CoV-2 main protease (Mpro), showing that our method can distinguish active compounds from inactive ones using only structural data of fragment-protein complexes, without any activity data. Further, we prospectively found novel hits for Mpro and the Mac1 domain of SARS-CoV-2 non-structural protein 3. More broadly, our results demonstrate how unsupervised machine learning helps interpret high throughput crystallography data to rapidly discover of potent chemical modulators of protein function. Paper - https://www.biorxiv.org/content/10.11... Speaker: William McCorkindale -   / willmccorki1   Twitter Prudencio:   / tossouprudencio   Twitter Therence:   / therence_mtl   Twitter Jonny:   / hsu_jonny   Twitter Valence Discovery:   / valence_ai   ~ Chapters: 00:00 - Intro 01:41 - Outline 06:48 - Fragment to Hit: ML Approaches and Challenges 11:07 - Introduction to FRagment Ensemble SCOring (FRESCO) 13:51 - Pharmacophore Modeling 20:29 - Diving Deep into FRESCO 24:42 - Retrospective Study - COVID Moonshot 34:55 - Screening Workflow 36:22 - Results 40:44 - Discussion 42:12 - Outlook and Conclusion 45:41 - Q&A