Generative AI in
Drug Discovery

Monthly lecture series, hosted by

Michael Bronstein and Bruno Correia

What We Cover

Dive into the applications of Generative AI across the many areas of drug discovery with some of the world's leading researchers

Series Objectives: The lecture series invites researchers from academia and industry to give new perspectives on AI in Drug Discovery, covering applications across small molecules, biologics, and more.
Topics: Protein Language Models
Protein Dynamics
Protein Design
Protein Engineering
Geometric Deep Learning
Retrosynthesis
Protein Folding
Protein-Ligand Docking
Benchmarking
AI for Science
Protein-Protein Docking
Molecular Generative Models
New Probabilistic Generative Models
Integration of Large Scale Experimental Data

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Upcoming Events

No lectures available on November 8, 2024

November 2024

SMTWTFS

Latest Lectures

Dive into the premiere episodes of a monthly lecture series dedicated to Generative AI in Drug Discovery

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The Road to Biology 2.0 Will Pass Through Black-Box Data

Michael Bronstein and Luca Naef

This year marks perhaps the zenith of expectations for AI-based breakthroughs in biology, transforming it into an engineering discipline that is programmable, predictable, and replicable. Drawing insights from AI breakthroughs in perception, natural language, and protein structure prediction, we endeavour to pinpoint the characteristics of biological problems that are most conducive to being solved by AI techniques.

Generative AI in
Drug Discovery

No lectures available on November 8, 2024

MDGEN: Generative Modeling of Molecular Dynamics Trajectories

PINDER and PLINDER: New Benchmarks for Predicting Protein-Protein and Protein-Ligand Interactions

FoldFlow-2: Sequence-Augmented SE(3)-Flow Matching For Conditional Protein Backbone Generation

Metric Flow Matching for Smooth Interpolations on the Data Manifold

Deep Confident Steps to New Pockets: Strategies for Docking Generalization

DNA Foundation Modeling From Molecular to Genome Scale

Generative Flows on Discrete State-Spaces: Enabling Multimodal Flows with Applications to Protein Co-Design

SE(3)-Stochastic Flow Matching for Protein Backbone Generation

Chroma: Illuminating Protein Space With a Programmable Generative Model

Flow Matching for Generative Modeling

Evaluating AI Models for Structure-Based Drug Design Based on Physical Constraints

Harnessing Geometry For Molecular Design

The Road to Biology 2.0 Will Pass Through Black-Box Data

Generative AI in Drug Discovery

No lectures available on November 8, 2024

MDGEN: Generative Modeling of Molecular Dynamics Trajectories

PINDER and PLINDER: New Benchmarks for Predicting Protein-Protein and Protein-Ligand Interactions

FoldFlow-2: Sequence-Augmented SE(3)-Flow Matching For Conditional Protein Backbone Generation

Metric Flow Matching for Smooth Interpolations on the Data Manifold

Deep Confident Steps to New Pockets: Strategies for Docking Generalization

DNA Foundation Modeling From Molecular to Genome Scale

Generative Flows on Discrete State-Spaces: Enabling Multimodal Flows with Applications to Protein Co-Design

SE(3)-Stochastic Flow Matching for Protein Backbone Generation

Chroma: Illuminating Protein Space With a Programmable Generative Model​

Flow Matching for Generative Modeling

Evaluating AI Models for Structure-Based Drug Design Based on Physical Constraints

Harnessing Geometry For Molecular Design

The Road to Biology 2.0 Will Pass Through Black-Box Data

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Generative AI in
Drug Discovery

Chroma: Illuminating Protein Space With a Programmable Generative Model