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.
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.
Pre-training via Denoising for Molecular Property Prediction
Explore pre-training via denoising for molecular property prediction, learning force fields from equilibrium structures to improve generalization and achieve state-of-the-art results on QM9 dataset.
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.
Fragment-Based Hit Discovery via Unsupervised Learning of Fragment-Protein Complexes
Explore unsupervised machine learning for fragment-based hit discovery in drug development. Learn about FRESCO method, pharmacophore modeling, and its application in COVID-19 research.
A Computational Framework for Solving Wasserstein Lagrangian Flows
Explore a computational framework for solving Wasserstein Lagrangian flows, covering optimal transport variations and applications in single-cell trajectory inference.
Learning Representations on Biological Data with Weakly Supervised Learning
Explore weakly supervised learning for biological data representations. Discover PeML method for improved similarity metrics in perturbational experiments, enhancing mechanism of action identification and downstream analysis tasks.
PFGM++: Unlocking the Potential of Physics-Inspired Generative Models
Explore PFGM++, a physics-inspired generative model unifying diffusion models and PFGMs. Learn about its flexible embedding, unbiased objectives, and superior performance on image datasets.
Transformers Meet Directed Graphs - Exploring Direction-Aware Positional Encodings
Explore direction-aware positional encodings for transformers on directed graphs, enhancing performance in code understanding and sorting network correctness testing.
Improving and Generalizing Flow-Based Generative Models with Minibatch Optimal Transport
Explore advanced techniques for improving continuous normalizing flows, including generalized conditional flow matching and optimal transport CFM, enhancing generative modeling for various applications.
Generative Language Models in Molecular Discovery: Regression Transformer, GT4SD and Beyond
Explore generative language models for molecular discovery, focusing on the Regression Transformer and GT4SD toolkit. Learn about multitask approaches bridging regression and conditional generation for scientific applications.
Improvements on GFlowNets Applied to Molecular Discovery
Explore recent advancements in GFlowNets for complex molecular design, including multi-objective optimization, improved training strategies, and potential applications in scientific discovery and active learning.