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.
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.
Leveraging Molecular ML and Property Prediction in Drug Design
Explore cutting-edge molecular machine learning techniques for drug design, focusing on property prediction to enhance pharmaceutical development.
Flow Annealed Importance Sampling Bootstrap - Machine Learning for Complex Distributions
Explore Flow Annealed Importance Sampling Bootstrap for approximating complex target distributions. Learn about normalizing flows, annealed importance sampling, and applications to molecular systems.
Artificial Chemical Intelligence: AI for Chemistry and Chemistry for AI
Explore the integration of AI with chemistry, focusing on trustworthy frameworks for molecular discovery. Learn about innovative approaches to predict mechanisms and overcome challenges in computational chemistry.
Crippen's logP as a Quantitative Molecular Benchmark for Explainable AI Heatmaps
Explore Crippen's logP as a quantitative benchmark for explainable AI heatmaps in molecular modeling. Learn about atomic attribution methods and their applications in chemical property prediction.
MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields
Explore higher-order equivariant message passing neural networks for fast, accurate force fields in computational chemistry. Learn about MACE's innovative approach using four-body messages to enhance efficiency and accuracy.
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.
Smooth, Exact Rotational Symmetrization for Deep Learning on Point Clouds
Explore rotational symmetrization for deep learning on point clouds, enhancing AI models for chemical and materials modeling with exact compliance to physical constraints.
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.
The Future of AI for Drug Discovery
Explore the future of AI in drug discovery with leading experts discussing challenges, opportunities, and practical applications of machine learning in molecular research.
Accelerating Cryptic Pocket Discovery with Deep Learning
Explore deep learning techniques for accelerating cryptic pocket discovery in protein structures, focusing on simulation, key degrees of freedom, and tools like PocketMiner and AlphaFold.
Machine Learning for Molecular Structures and Drug Discovery
Explore machine learning applications in molecular structures, generative models, and protein analysis. Learn about tools like LigDream, TorchMD, and PlayMolecules for drug discovery and chemistry research.
Applying Active Learning in Drug Discovery - There's No Free Lunch, but You Can Get a Discount
Explore active learning applications in drug discovery, from prioritizing molecules for synthesis to virtual screening of large libraries, with insights on machine learning's impact across the process.
Calibration and Generalizability of Probabilistic Models on Low-Data Chemical Datasets
Explore calibration and generalizability of probabilistic models on small chemical datasets. Gain insights into model selection and feature choice for molecular property prediction and design.
Machine Learning for Multi-Scale Molecular Simulation and Design
Explore multi-scale molecular simulation techniques using machine learning for accelerated MD simulations and MOF design, focusing on coarse-grained modeling and graph neural networks.