Predictive Chemistry Augmented with Text Retrieval
Explore TextReact: a novel method enhancing predictive chemistry models with text retrieval from literature, improving reaction condition recommendations and retrosynthesis predictions.
Structured State-Space Sequence Models for De Novo Drug Design
Explore the potential of Structured State-Space Sequence (S4) models in advancing chemical language modeling for de novo drug design, including bioactive compound identification and natural product design.
Conditional Graph Information Bottleneck for Molecular Relational Learning
Explore a novel framework for molecular relational learning using graph neural networks to detect core subgraphs and predict interactions between molecular pairs.
Timewarp: Transferable Acceleration of Molecular Dynamics by Time-Coarsened Dynamics
Explore Timewarp: a transferable method for accelerating molecular dynamics simulations using normalizing flows, enhancing sampling efficiency for equilibrium properties of molecular systems.
Generalized Biomolecular Modeling and Design with RoseTTAFold All-Atom
Explore advanced biomolecular modeling using RoseTTAFold All-Atom. Learn to predict and design complex biological assemblies, including proteins, nucleic acids, and small molecules, with applications in drug discovery and synthetic biology.
Timewarp: Transferable Acceleration of Molecular Dynamics by Learning Time-Coarsened Dynamics
Explore accelerated molecular dynamics simulation using Timewarp, a transferable AI method for efficient sampling of molecular systems and discovery of metastable states.
Harmonic Self-Conditioned Flow Matching for Multi-Ligand Docking and Binding Site Design
Explore advanced AI techniques for protein-ligand binding structure generation and binding site design, improving drug discovery and molecular interaction modeling.
Large Language Molecular Representation and Learning
Explore molecular machine learning's potential in drug discovery, focusing on representation challenges, self-supervised learning, and leveraging pre-trained language models for property prediction.
PepFlow: Direct Conformational Sampling From Peptide Energy Landscapes
Explore PepFlow, a deep learning approach for direct conformational sampling of peptides. Learn about its modular design, hypernetwork integration, and applications in drug discovery.
Fostering the Development of Impactful AI Models in Drug Discovery - ICML 2023 Panel
Explore challenges and solutions in bridging academic AI research with real-world drug discovery applications through expert panel insights on benchmarking, data sharing, and industry-academia collaboration.
Geometric Deep Learning Framework for De Novo Genome Assembly
Explore a novel geometric deep learning framework for de novo genome assembly, enhancing path identification in complex assembly graphs for improved genomic sequence reconstruction.
Binding Affinity Prediction with ML-Based Docking - Lab 2
Explore ML-based docking for binding affinity prediction in drug discovery, featuring insights from experts Stephan Thaler and Cristian Gabellini.
Protein Folding and Design - Day 5
Explore protein folding and design techniques with Alex Tong, delving into advanced machine learning applications for drug discovery and structural biology.
Target Deconvolution Explanation - Lab 4
Explore target deconvolution techniques in drug discovery through hands-on lab exercises led by experts Ali Denton and Kristina Ulicna.
Accelerate Atomistic Simulations, Sampling, and Dynamics - Day 2
Explore advanced techniques for accelerating atomistic simulations, enhancing sampling methods, and optimizing dynamics in drug discovery research.
Synthesizability and Molecular Synthesis - Day 3
Explore synthesizability and molecular synthesis techniques with Connor Coley, gaining insights into advanced drug discovery methods and machine learning applications.
Coarse Grained Biological Systems - Day 2
Explore coarse-grained biological systems in drug discovery with Jacopo Venturin, delving into advanced machine learning applications for pharmaceutical research.
Modeling Population Dynamics - Day 4
Explore population dynamics modeling techniques for drug discovery, focusing on innovative approaches and their applications in pharmaceutical research.
Open-Source Initiatives and Benchmarking Efforts in Machine Learning for Drug Discovery - Day 5
Explore open-source initiatives and benchmarking efforts in machine learning for drug discovery with Karmen Condic-Jurkic from Valence Labs.
Virtual Screening Techniques for Drug Discovery - Lab 1
Explore virtual screening techniques for drug discovery through hands-on lab exercises guided by industry experts.
Virtual Screening Explanation - Lab 1
Explore virtual screening techniques for drug discovery with experts Lu Zhu and Cas Wognum in this recorded session from the 2024 ML for Drug Discovery Summer School.