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Explore advanced neural network interatomic potentials for molecular dynamics simulations using Stability-Aware Boltzmann Estimator Training to enhance stability and accuracy across various systems and architectures.
Explore PDGRAPHER, a novel graph neural network model for phenotype-driven drug discovery. Learn how it predicts perturbagens to reverse disease effects, offering faster and more efficient therapeutic target identification.
Explore geometric graph neural networks for 3D atomic systems, covering fundamental concepts, architectures, datasets, and applications in computational modeling of molecules, proteins, and materials.
Explore HyperPCM, a robust method for predicting drug-target interactions using hypernetworks. Learn about its state-of-the-art performance on benchmarks and excellence in zero-shot inference for unseen protein targets.
Explore advanced diffusion models for generating transition state structures in chemical reactions, accelerating reaction mechanism discovery and network exploration.
Explore MFBind, a multi-fidelity approach integrating docking and binding free energy simulations for improved drug discovery generative modeling. Learn its advantages over current methods.
Explore causal aggregation challenges, macro interventions, and micro-realism in statistical analysis. Gain insights into ill-defined causality and its impact on confounding in aggregated variables.
Explore advancements in synthesis planning evaluation through syntheseus and retro-fallback, enhancing molecule discovery with probabilistic uncertainty and new search algorithms.
Explore MultiDiffusion: a framework for controllable image generation using pre-trained diffusion models. Learn about panorama creation, high-resolution outputs, and versatile user controls without retraining.
Explore additive decoders for latent variable identification and out-of-support image generation in representation learning. Gain insights into nonlinear ICA and object-centric representation learning.
Explore InfoCORE, an innovative approach to remove batch effects in drug screening data, enhancing molecular representations for improved property prediction and molecule-phenotype retrieval.
Explore machine learning-accelerated molecular docking using equivariant scalar fields and fast Fourier transforms for efficient drug discovery and virtual screening.
Explore reward fine-tuning for dynamical generative models through stochastic optimal control, featuring a novel Adjoint Matching algorithm for improved consistency and realism.
Explore advanced probabilistic inference techniques for language models using Twisted Sequential Monte Carlo, enhancing LLM capabilities and safety measures.
Explore a novel geometric deep learning framework for de novo genome assembly, enhancing path identification in complex assembly graphs for improved genomic sequence reconstruction.
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