Myriad LLM Approaches to Drug Discovery Generative Artificial Intelligence
Explore diverse LLM approaches in drug discovery GenAI, empowering medicinal chemists to enhance the process through informed prompts and molecule generation techniques.
Apple On-Device GenAI for Lead Optimization in Drug Discovery - MacOS and iOS
Explore on-device GenAI capabilities for lead optimization in drug discovery using Apple devices. Learn about performance, speed, and memory for running AI models locally.
Chemical Foundation Models for Drug Discovery
Explore chemical foundation models for drug discovery, focusing on innovative approaches and potential breakthroughs in pharmaceutical research.
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.
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.
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.
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.
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.
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.