5 papers
Scalable Inference-Time Annealing with Surrogate Likelihood Estimators
Daniel Peñaherrera, Rishal Aggarwal, David Ryan Koes
A long standing challenge in computational chemistry and biophysics is efficiently sampling the Boltzmann distribution of molecules. Advances in generative modeling have been propo…
Reactive Flux Matching: Mechanism Discovery and Adaptive Sampling of Rare Events
Rishal Aggarwal, David Ryan Koes, Nicholas M. Boffi +1
Path sampling methods generate ensembles of reactive trajectories connecting metastable states, but extracting mechanistic insight from these data remains nontrivial. We introduce…
OMTRA: A Multi-Task Generative Model for Structure-Based Drug Design
Ian Dunn, Liv Toft, Tyler Katz +4
Structure-based drug design (SBDD) focuses on designing small-molecule ligands that bind to specific protein pockets. Computational methods are integral in modern SBDD workflows an…
BoltzNCE: Learning Likelihoods for Boltzmann Generation with Stochastic Interpolants and Noise Contrastive Estimation
Rishal Aggarwal, Jacky Chen, Nicholas M. Boffi +1
Efficient sampling from the Boltzmann distribution given its energy function is a key challenge for modeling complex physical systems such as molecules. Boltzmann Generators addres…
FlowMol3: Flow Matching for 3D De Novo Small-Molecule Generation
Ian Dunn, David R. Koes
A generative model capable of sampling realistic molecules with desired properties could accelerate chemical discovery across a wide range of applications. Toward this goal, signif…