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.
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.
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.
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.
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.
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.
Training Neural Network Potentials: Bayesian and Simulation-based Approaches
Explore advanced techniques for training neural network potentials in molecular dynamics simulations, including data-efficient methods, experimental data integration, and uncertainty quantification for reliable results.
Accelerating Cryptic Pocket Discovery Using AlphaFold and Markov State Modeling
Explore cryptic pocket discovery using AlphaFold and Markov State Modelling. Learn about Plasmepsin II, transition states, and activation mechanisms in drug discovery.
Chemically Transferable Generative Backmapping of Coarse-Grained Proteins
Explore innovative machine learning techniques for generative backmapping of coarse-grained proteins, focusing on transferability, reliability, and efficiency in molecular simulations.
Cell Morphology-Guided De Novo Hit Design by Conditioning GANs on Phenotypic Image Features
Explore cell morphology-guided de novo hit design using GANs conditioned on phenotypic image features for efficient bioactive molecule development in drug discovery.
Path Integral Stochastic Optimal Control for Sampling Transition Paths in Molecular Dynamics
Explore stochastic optimal control for sampling molecular transition paths, considering full geometry without relying on collective variables. Applicable to larger proteins like Polyproline and Chignolin.
Interpretable Chirality-Aware Graph Neural Networks for QSAR Modeling in Drug Discovery
Explore an innovative Graph Neural Network approach for drug discovery, incorporating molecular chirality and offering interpretable results to enhance quantitative structure-activity relationship modeling.
Protein Folding Algorithms - Lecture 9
Explore protein folding algorithms, their applications, and computational approaches in structural biology and drug discovery.
Differentiable Simulations for Enhanced Sampling of Rare Events
Explore deep learning applications in molecular simulations, including surrogate functions, sampling complex distributions, and time-propagation of differential equations. Learn about active learning, generative models, and reaction path finding.
Neural Network Potentials for Low-Energy 3D Structure Generation and Reactivity Prediction
Explore neural network potentials for efficient 3D structure generation and reactivity prediction in molecular modeling, featuring Auto3D package and ANI-2xt model advancements.
Molecule Representation Learning: A Perspective from Topology, Geometry, and Textual Description
Explore molecule representation learning through topology, geometry, and textual description. Gain insights into AI-driven drug discovery and molecular modeling techniques.