Fragment-Based Hit Discovery via Unsupervised Learning of Fragment-Protein Complexes
Explore unsupervised machine learning for fragment-based hit discovery in drug development. Learn about FRESCO method, pharmacophore modeling, and its application in COVID-19 research.
Calibration and Generalizability of Probabilistic Models on Low-Data Chemical Datasets
Explore calibration and generalizability of probabilistic models on small chemical datasets. Gain insights into model selection and feature choice for molecular property prediction and design.
An Optimal Control Perspective on Diffusion-Based Generative Modeling
Explore optimal control theory's connection to diffusion-based generative modeling. Gain insights into stochastic differential equations, Hamilton-Jacobi-Bellman equations, and novel sampling methods for unnormalized densities.
A Computational Framework for Solving Wasserstein Lagrangian Flows
Explore a computational framework for solving Wasserstein Lagrangian flows, covering optimal transport variations and applications in single-cell trajectory inference.
Learning Representations on Biological Data with Weakly Supervised Learning
Explore weakly supervised learning for biological data representations. Discover PeML method for improved similarity metrics in perturbational experiments, enhancing mechanism of action identification and downstream analysis tasks.
Machine Learning for Multi-Scale Molecular Simulation and Design
Explore multi-scale molecular simulation techniques using machine learning for accelerated MD simulations and MOF design, focusing on coarse-grained modeling and graph neural networks.
Action Matching: Learning Stochastic Dynamics from Samples
Explore Action Matching for learning stochastic dynamics from samples. Discover applications in quantum systems, biology, and generative modeling. Gain insights into novel approaches for simulating trajectories.
Equivariant Flow Matching for Deep Generative Models in Physics
Explore equivariant flow matching for efficient, simulation-free training of equivariant CNFs in physics and drug discovery, improving sampling efficiency and scalability for many-particle systems.
Predicting Equilibrium Distributions for Molecular Systems with Deep Learning
Explore deep learning techniques for predicting equilibrium distributions in molecular systems, focusing on the novel Distributional Graphormer framework and its applications in various molecular tasks.
PFGM++: Unlocking the Potential of Physics-Inspired Generative Models
Explore PFGM++, a physics-inspired generative model unifying diffusion models and PFGMs. Learn about its flexible embedding, unbiased objectives, and superior performance on image datasets.
Transformers Meet Directed Graphs - Exploring Direction-Aware Positional Encodings
Explore direction-aware positional encodings for transformers on directed graphs, enhancing performance in code understanding and sorting network correctness testing.
Improving and Generalizing Flow-Based Generative Models with Minibatch Optimal Transport
Explore advanced techniques for improving continuous normalizing flows, including generalized conditional flow matching and optimal transport CFM, enhancing generative modeling for various applications.
Generative Language Models in Molecular Discovery: Regression Transformer, GT4SD and Beyond
Explore generative language models for molecular discovery, focusing on the Regression Transformer and GT4SD toolkit. Learn about multitask approaches bridging regression and conditional generation for scientific applications.
Clifford Group Equivariant Neural Networks
Explore Clifford Group Equivariant Neural Networks for E(n)-equivariant network construction. Discover theoretical foundations, methodology, and applications in various dimensions and tasks.
Improvements on GFlowNets Applied to Molecular Discovery
Explore recent advancements in GFlowNets for complex molecular design, including multi-objective optimization, improved training strategies, and potential applications in scientific discovery and active learning.
Is Distance Matrix Enough for Geometric Deep Learning?
Explore geometric deep learning limitations and k-DisGNNs' potential in capturing high-order geometric information, unifying models, and achieving universal function approximation for geometric graphs.
Learning Local Equivariant Representations for Large-Scale Atomistic Dynamics
Explore Allegro, a local equivariant deep learning interatomic potential for efficient, accurate, and scalable atomistic dynamics simulations, including applications in biomolecular systems.
Mirror Diffusion Models for Constrained and Watermarked Generation
Explore Mirror Diffusion Models for generating data on constrained sets, enhancing watermarking capabilities, and improving AI safety in drug discovery applications.
Designing Losses for Data-Free Training of Normalizing Flows on Boltzmann Distributions
Explore advanced techniques for training normalizing flows on Boltzmann distributions without data, addressing mode collapse and optimizing high-dimensional distributions in molecular configuration generation.
Stochastic Interpolants: A Unifying Framework for Flows and Diffusions - Lecture
Explore a unified framework for generative models, bridging flow-based and diffusion-based methods using stochastic interpolants. Learn about probability flow equations, likelihood estimation, and applications in AI for drug discovery.
Tackling the Challenges of High-Performance Graph Analytics at Compiler Level
Explore COMET, a DSL and compiler framework for efficient tensor algebra code generation, focusing on optimizations for high-performance graph analytics on heterogeneous systems.