Phenomics in Drug Discovery: Microscopy and Machine Learning - Day 4
Explore phenomics in drug discovery through microscopy and machine learning techniques with expert Anne Carpenter.
Binding Affinity Prediction with Machine Learning-Based Docking - Lab 2
Explore ML-based docking techniques for predicting binding affinity in drug discovery, featuring insights from experts Stephan Thaler and Cristian Gabellini.
Exploring Molecular Space and Active Learning in Drug Discovery
Explore molecular space and active learning techniques for drug discovery with AI pioneer Yoshua Bengio's insights from the 2024 ML Summer School at Mila.
Lab 3 - De Novo Generation Explanation
Explore de novo molecule generation techniques with experts Emmanuel Bengio and Julien Roy in this recorded session from the 2024 Machine Learning for Drug Discovery Summer School.
Learning ML Interatomic Potentials - Day 2
Explore ML interatomic potentials for drug discovery with expert Gianni De Fabritiis. Gain insights into advanced techniques and applications in computational chemistry.
LLMs in Drug Discovery - Day 5
Explore the application of Large Language Models in drug discovery, focusing on innovative approaches and potential breakthroughs in pharmaceutical research.
Harnessing Geometric ML for Molecular Design - Day 3
Explore geometric machine learning techniques for innovative molecular design in drug discovery with expert Michael Bronstein.
Generative Models of Molecular Structures - Day 3
Explore generative models for molecular structures, focusing on applications in drug discovery and computational chemistry.
Machine Learning in Structure-Based Drug Discovery - Day 2
Explore machine learning applications in structure-based drug discovery, focusing on innovative techniques and their impact on pharmaceutical research.
Diffusion Forcing: Next-token Prediction Meets Full-Sequence Diffusion
Explore Diffusion Forcing, a novel training paradigm combining next-token prediction and full-sequence diffusion for enhanced sequence generation and decision-making in AI applications.
Polaris - Industry-Led Initiative to Critically Assess ML for Real-World Drug Discovery
Explore an industry-led initiative critically assessing machine learning's impact on real-world drug discovery through the Polaris project.
Roughness of Molecular Property Landscapes and Its Impact on Modellability
Explore molecular property landscapes' roughness and its impact on modellability. Learn about the Roughness Index (ROGI) and its correlation with machine learning model performance in molecular discovery and drug design.
Introducing Bioptic - Machine Learning for Drug Discovery
Explore Bioptic's architecture, training, and scaling for machine learning in drug discovery with Vlad Vinograv.
Molecular Machine Learning Conference 2023 - Opening Remarks
Explore opening remarks from Yoshua Bengio and Dominique Beaini at MoML 2023, covering conference highlights, tributes, and the future of molecular machine learning.
Contextual AI Models for Single-cell Protein Biology
Explore PINNACLE, a geometric deep learning approach generating context-aware protein representations for enhanced understanding of protein function and interactions across biological contexts.
Learning to Group Auxiliary Datasets for Molecule Prediction
Explore MolGroup: a novel approach to optimize auxiliary dataset selection in molecular machine learning, enhancing model performance through intelligent data grouping.
Local Search GFlowNets - Enhancing Sampling Quality for Drug Discovery
Explore Local Search GFlowNets, a novel algorithm enhancing sampling quality for structured sequence generation in drug discovery and other fields through improved targeting of high-reward regions.
Diffusion Models for Sampling Rare Events in Chemistry
Explore advanced diffusion models for generating transition state structures in chemical reactions, accelerating reaction mechanism discovery and network exploration.
Manifold Diffusion Fields - Learning Generative Models of Continuous Functions on Riemannian Manifolds
Explore Manifold Diffusion Fields for generating continuous functions on Riemannian manifolds. Learn about spectral geometry, intrinsic coordinate systems, and applications in AI for drug discovery.
Multiflow: Protein Structure and Sequence Co-generation
Explore protein structure and sequence co-generation using Discrete Flow Models and Multiflow, a novel approach combining discrete and continuous data for improved AI-driven drug discovery.
Geometric Machine Learning for Molecular Medicine
Explore geometric machine learning applications in molecular medicine, focusing on AI models for therapeutic science and drug repurposing using knowledge graphs and contextual approaches.