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.
OpenFold: Lessons Learned and Insights from Rebuilding and Retraining AlphaFold2
Explore insights from rebuilding AlphaFold2, including performance analysis, learning mechanisms, and current progress in protein structure prediction.
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.
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.
The Molecular Twin Platform: A Multi-Omic Approach for Predicting Accurate Clinical Outcomes
Explore a multi-omic AI platform combining tumor and host data to predict disease survival outcomes using advanced biomarker panels and machine learning techniques.
Scaling Carbon Removal to Gigatons - Building the CDR Industry
Explore the carbon removal industry's growth, workforce challenges, and strategies for building a thriving sector capable of scaling to gigatons, based on insights from the 2024 CDR Salary Report.
New Methods for EOR Optimization Using Microfluidics
Explore microfluidics-based optimization for heavy oil recovery, focusing on chemical spend efficiency and increased oil extraction in Alberta and Saskatchewan fields.
Towards Higher-level Abstractions for Molecular Programming
Explore higher-level abstractions for molecular programming using chemical reaction networks, inspired by functional reactive programming. Build modular CRN programs with a Haskell library.
The Algorithm for Precision Medicine
Explore the intersection of algorithms and precision medicine, uncovering innovative approaches to personalized healthcare and treatment strategies.
Strange Loops: Journeys in Declarative Logic Programming in Genomics and Beyond
Explore declarative logic programming applications in genomics and other fields through Christopher Mungall's insightful journey and discoveries.
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.
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.
Benchmark and Critical Evaluation for ML Force Fields with Molecular Simulations
Explore benchmarking and critical evaluation of machine learning force fields in molecular simulations, focusing on realistic MD trajectories beyond force prediction accuracy.
ProtMD: Incorporating Conformation Flexibility for Drug Binding via Pretraining on MD Simulations
Explore ProtMD, a novel approach incorporating protein flexibility in drug binding predictions using molecular dynamics simulations and self-supervised learning techniques for improved accuracy.
DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking
Explore DiffDock, a novel diffusion-based approach to molecular docking that outperforms traditional and deep learning methods in predicting ligand-protein binding structures for drug design.
Equivariant 3D-Conditional Diffusion Models for Molecular Linker Design
Explore equivariant 3D-conditional diffusion models for molecular linker design in drug discovery. Learn about DiffLinker's innovative approach to connecting multiple fragments and its applications in real-world scenarios.
Predicting Single-Cell Perturbation Responses for Unseen Drugs
Explore single-cell perturbation response prediction for novel drugs using an encoder-decoder architecture and transfer learning, enhancing drug discovery efficiency through in-silico hypothesis generation.
The Elusive Phase Operator: Unraveling Mysteries in Quantum Harmonic Oscillators
Explore the evolution and challenges of quantum phase operators, from Dirac's dilemma to recent advancements, unraveling their mysteries and potential applications in quantum computing.