A5024532588- Computational Drug Discovery Methods
- vaccines and immunoinformatics approaches
- Protein Degradation and Inhibitors
- Synthesis and biological activity
- Click Chemistry and Applications
- SARS-CoV-2 and COVID-19 Research
- CAR-T cell therapy research
- Machine Learning in Materials Science
- Cell Image Analysis Techniques
Weizmann Institute of Science
2020-2024
We report the results of COVID Moonshot, a fully open-science, crowdsourced, and structure-enabled drug discovery campaign targeting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease. discovered noncovalent, nonpeptidic inhibitor scaffold with lead-like properties that is differentiated from current protease inhibitors. Our approach leveraged crowdsourcing, machine learning, exascale molecular simulations, high-throughput structural biology chemistry. generated...
Abstract We report the results of COVID Moonshot , a fully open-science, crowd sourced, structure-enabled drug discovery campaign targeting SARS-CoV-2 main protease. discovered non-covalent, non-peptidic inhibitor scaffold with lead-like properties that is differentiated from current protease inhibitors. Our approach leveraged crowdsourcing, machine learning, exascale molecular simulations, and high-throughput structural biology chemistry. generated detailed map plasticity protease,...
Abstract Designing covalent inhibitors is a task of increasing importance in drug discovery. Efficiently designing irreversible inhibitors, though, remains challenging. Here, we present covalentizer , computational pipeline for creating based on complex structures targets with known reversible binders. For each ligand, create custom-made focused library analogs. We use docking, to dock these tailored libraries and find those that can bind covalently nearby cysteine while keeping some the...
High throughput and rapid biological evaluation of small molecules is an essential factor in drug discovery development. Direct-to-Biology (D2B), whereby compound purification foregone, has emerged as a viable technique time efficient screening, specifically for PROTAC design evaluation. However, one notable limitation the prerequisite high yielding reactions to ensure desired indeed responsible activity. Herein, we report machine learning based yield-assay deconfounder capable deconvoluting...
Augmenting direct-to-biology workflows with a new machine learning framework.
High throughput and rapid biological evaluation of small molecules is an essential factor in drug discovery development. Direct-to-Biology (D2B), whereby compound purification foregone, has emerged as a viable technique time efficient screening, specifically for PROTAC design evaluation. However, one notable limitation the prerequisite high yielding reactions to ensure desired indeed responsible activity. Herein, we report machine learning based yield-assay deconfounder capable deconvoluting...