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.
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.
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.
Молекулярно-динамічні моделювання колективної динаміки іон-гідратної оболонки подвійної спіралі ДНК
Дослідіть молекулярно-динамічне моделювання колективної поведі нки іон-гідратної оболонки ДНК та її вплив на структурну динаміку подвійної спіралі.
Метод класичної молекулярної динаміки в моделюванні властивостей молекули подвійної спіралі ДНК
Dive into classical molecular dynamics methods and their application in modeling DNA double helix properties, exploring computational approaches in modern biophysics research.
Моделювання біохімічних процесів за допомогою гібридних квантово-механічних/молекулярно-механічних методів QM/MM в NAMD та ORCA
Дослідіть моделювання біохімічних процесів з використанням гібридних QM/MM методів у NAMD та ORCA для поглибленого аналізу молекулярних систем.
Reinforcement Learning-Based Fine-Tuning of Diffusion Models for Drug Discovery
Dive into advanced reinforcement learning techniques for fine-tuning diffusion models, focusing on accelerated drug discovery, bioinformatics, and molecular compound predictions.
Universality Classes for 1+1 Dimensional Systems
Explore universality classes in 1+1 dimensional systems, focusing on weak universality conjecture, graphical construction, and links to Brownian web. Gain insights into complex systems modeling.
How Will Quantum Computing Change the Technology Landscape? - Bruce Davie, Systems Approach, LLC
Explore quantum computing's potential impact on technology, from achieving quantum supremacy to revolutionizing cryptography, optimization, and machine learning, while addressing challenges and separating hype from reality.
Quantum Computing: Untangling the Hype
Experts discuss quantum computing's potential, workings, and impact on fields like cryptography and molecular modeling, separating hype from reality in this comprehensive exploration.
Phase Transitions for Mean Field Limits of Noisy Interacting Agents
Explore phase transitions in mean field limits of noisy interacting agents, examining complex systems modeling and statistical approaches for accurate predictions in various scientific fields.
Coarse-Graining Stochastic Dynamics
Explore techniques for simplifying complex stochastic systems through coarse-graining, enabling practical computation and accurate prediction in various scientific fields.
Understanding the Geopolitical Structure of the USA
Explore statistical approaches for analyzing geopolitical structures in the USA, combining data analysis with complex systems modeling to uncover crucial features and patterns.
Learning Models by Making Them Interact
Explore new approaches to data-driven modeling of complex systems, combining statistical inference, high-throughput computation, and physical laws for more accurate predictions in various scientific fields.
Exploring Molecular Space and Active Learning in Drug Discovery
Explore molecular space and active learning techniques for drug discovery with AI pioneer Yoshua Bengio's insights from the 2024 ML Summer School at Mila.
Learning ML Interatomic Potentials - Day 2
Explore ML interatomic potentials for drug discovery with expert Gianni De Fabritiis. Gain insights into advanced techniques and applications in computational chemistry.
LLMs in Drug Discovery - Day 5
Explore the application of Large Language Models in drug discovery, focusing on innovative approaches and potential breakthroughs in pharmaceutical research.
Machine Learning in Structure-Based Drug Discovery - Day 2
Explore machine learning applications in structure-based drug discovery, focusing on innovative techniques and their impact on pharmaceutical research.
Polaris - Industry-Led Initiative to Critically Assess ML for Real-World Drug Discovery
Explore an industry-led initiative critically assessing machine learning's impact on real-world drug discovery through the Polaris project.