38 citations · 88 across the 10 of their papers we have counts for
11 papers
Nemotron 3 Ultra: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoning
NVIDIA, :, Aaron Blakeman +572
We introduce Nemotron 3 Ultra, a 550 billion total and 55 billion active parameter Mixture-of-Experts Hybrid Mamba-Attention language model. We pre-trained Nemotron 3 Ultra on 20 t…
Probabilistic Contrastive Pretraining for Multi-task ADME Property Prediction
Yifan Xue, Srimukh Prasad Veccham, Saee Paliwal +2
Accurate prediction of absorption, distribution, metabolism, and excretion (ADME) properties is critical to drug discovery, but remains challenging because ADME endpoints are noisy…
Multitask finetuning and acceleration of chemical pretrained models for small molecule drug property prediction
Matthew Adrian, Yunsie Chung, Kevin Boyd +3
Chemical pretrained models, sometimes referred to as foundation models, are receiving considerable interest for drug discovery applications. The general chemical knowledge extracte…
Exploring Synthesizable Chemical Space with Iterative Pathway Refinements
Seul Lee, Karsten Kreis, Srimukh Prasad Veccham +5
A well-known pitfall of molecular generative models is that they are not guaranteed to generate synthesizable molecules. Existing solutions for this problem often struggle to effec…
GenMol: A Drug Discovery Generalist with Discrete Diffusion
Seul Lee, Karsten Kreis, Srimukh Prasad Veccham +6
Drug discovery is a complex process that involves multiple stages and tasks. However, existing molecular generative models can only tackle some of these tasks. We present Generalis…
Molecule Generation with Fragment Retrieval Augmentation
Seul Lee, Karsten Kreis, Srimukh Prasad Veccham +5
Fragment-based drug discovery, in which molecular fragments are assembled into new molecules with desirable biochemical properties, has achieved great success. However, many fragme…
BioNeMo Framework: a modular, high-performance library for AI model development in drug discovery
Peter St. John, Dejun Lin, Polina Binder +89
Artificial Intelligence models encoding biology and chemistry are opening new routes to high-throughput and high-quality in-silico drug development. However, their training increas…
Want to train KANS at scale? Now UKAN!
Alireza Moradzadeh, Srimukh Prasad Veccham, Lukasz Wawrzyniak +2
Kolmogorov-Arnold Networks (KANs) have recently emerged as a powerful alternative to traditional multilayer perceptrons. However, their reliance on predefined, bounded grids restri…
A Non-Perturbative Pairwise-Additive Analysis of Charge Transfer Contributions to Intermolecular Interaction Energies
Srimukh Prasad Veccham, Joonho Lee, Yuezhi Mao +2
Energy decomposition analysis (EDA) based on absolutely localized molecular orbitals (ALMOs) decomposes the interaction energy between molecules into physically interpretable compo…
Density Functionals for Hydrogen Storage: Defining the H2Bind275 Test Set with Ab Initio Benchmarks and Assessment of 55 Functionals
Srimukh Prasad Veccham, Martin Head-Gordon
Efficient and high capacity storage materials are indispensable for a hydrogen-based economy. In silico tools can accelerate the process of discovery of new adsorbent materials wit…
Making Many-Body Interactions Nearly Pairwise Additive: The Polarized Many-Body Expansion Approach
Srimukh Prasad Veccham, Joonho Lee, Martin Head-Gordon
The Many-Body Expansion (MBE) is a useful tool to simulate condensed phase chemical systems, often avoiding the steep computational cost of usual electronic structure methods. Howe…