Video Lectures (91 talks)
A curated collection of 91 research talks from cutting-edge ML + biology conferences — covering geometric deep learning, protein design, generative models for drug discovery, single-cell foundation models, and scientific machine learning. Talks are drawn from leading venues including AI for Science seminars, drug discovery workshops, the MoML conference, and foundation models symposia.
gRNAde: Geometric Deep Learning for 3D RNA Inverse Design
Contextual AI Models for Single-Cell Protein Biology
The Future of Chemistry is Self-Driving (Aspuru-Guzik)
AlphaFold and Beyond: Protein Structure Prediction with AI
Diffusion Models for Molecular Generation and Drug Design
Foundation Models for Scientific Discovery
Geometric Deep Learning on Molecular Structures
SE(3)-Equivariant Neural Networks for Molecular Systems
Graph Neural Networks for Antibody Design
Large Language Models Meet Molecular Science
Neural Scaling Laws for Scientific Machine Learning
Protein Language Models: From Sequence to Function
Score-Based Generative Models for Protein Design
Bayesian Optimization for Molecular Discovery
Message Passing Neural Networks for Quantum Chemistry
Reinforcement Learning for Retrosynthesis Planning
Flow Matching for Generative Modeling of Molecules
Neural ODEs for Molecular Dynamics Simulation
Active Learning for High-Throughput Screening
Multi-Scale Modeling with Graph Transformers
About These Talks
These 91 talks span four thematic collections: AI for Science research seminars covering the breadth of scientific ML, generative AI methods for drug discovery and molecular design, the Molecules and ML (MoML) 2024 conference, and foundation models for biology including single-cell genomics and protein language models. Together they provide a panoramic view of how machine learning is transforming the biological and chemical sciences.
All content is publicly available on YouTube. Talks are organized thematically to complement the ML for Science course structure.