2 citations · 2 across the 4 of their papers we have counts for
4 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…
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…
APObind: A Dataset of Ligand Unbound Protein Conformations for Machine Learning Applications in De Novo Drug Design
Rishal Aggarwal, Akash Gupta, U Deva Priyakumar
Protein-ligand complex structures have been utilised to design benchmark machine learning methods that perform important tasks related to drug design such as receptor binding site…