The Large Language Model agent workflow enables the LLM to invoke tool functions to increase the performance on specific scientific domain questions. To tackle large scale of scientific research, it requires access to computing resource and parallel computing setup. In this work, we implemented Parsl to the LangChain/LangGraph tool call setup, to bridge the gap between the LLM agent to the computing resource. Two tool call implementations were set up and tested on both local workstation and HPC environment on Polaris/ALCF. The first implementation with Parsl-enabled LangChain tool node queues the tool functions concurrently to the Parsl workers for parallel execution. The second configuration is implemented by converting the tool functions into Parsl ensemble functions, and is more suitable for large task on super computer environment. The LLM agent workflow was prompted to run molecular dynamics simulations, with different protein structure and simulation conditions. These results showed the LLM agent tools were managed and executed concurrently by Parsl on the available computing resource.

Protein-ligand binding [Clyde et al., 2023] refers to the process as shown in Figure 1 by which ligands--usually small molecules, ions, or proteins--generate signals by binding to the active sites of target proteins through intermolecular forces. This binding typically changes the conformation of target proteins, which then results in the realization, modulation, or alteration of protein functions. Therefore, protein-ligand binding plays a central role in most, if not all, important life processes. For example, oxygen molecules are bound and carried through the human body by proteins like hemoglobin, and then utilized for energy production, while nonsteroidal anti-inflammatory drugs (NSAIDs) like ibuprofen work by inhibiting the functionality of the cyclooxygenase (COX) enzyme that thus reducing the release of pain-causing substances in the body. The concept and importance of binding affinity prediction were first addressed in Böhm [1994]: given the 3D structures of a target protein and a potential ligand, the objective is to predict the binding constant of such a complex, along with the most probable binding pose candidates. The prediction of the binding site (the set of protein residues that have at least one non-hydrogen atom within 4.0 Å of a ligand's non-hydrogen atom [Khazanov and Carlson, 2013]) and affinity (binding constants such as inhibition or dissociation constants, or the concentration at 50% inhibition) are usually divided into two separate but related stages [Ballester and Mitchell, 2010a]. One notable motivation for constructing a good binding affinity predictor (or scoring function, as called in some earlier work) is the essential role that it plays in drug discovery [Liu et al., 2023, 2024a] and virtual screening [Meng et al., 2011, Pinzi and Rastelli, 2019, Sadybekov and Katritch, 2023]. Traditional drug discovery essentially involves a process of trial and error.

In the upcoming decade, deep learning may revolutionize the natural sciences, enhancing our capacity to model and predict natural occurrences. This could herald a new era of scientific exploration, bringing significant advancements across sectors from drug development to renewable energy. To answer this call, we present DeepSpeed4Science initiative (deepspeed4science.ai) which aims to build unique capabilities through AI system technology innovations to help domain experts to unlock today's biggest science mysteries. By leveraging DeepSpeed's current technology pillars (training, inference and compression) as base technology enablers, DeepSpeed4Science will create a new set of AI system technologies tailored for accelerating scientific discoveries by addressing their unique complexity beyond the common technical approaches used for accelerating generic large language models (LLMs). In this paper, we showcase the early progress we made with DeepSpeed4Science in addressing two of the critical system challenges in structural biology research.

We outline recent developments in artificial intelligence (AI) and machine learning (ML) techniques for integrative structural biology of intrinsically disordered proteins (IDP) ensembles. IDPs challenge the traditional protein structure-function paradigm by adapting their conformations in response to specific binding partners leading them to mediate diverse, and often complex cellular functions such as biological signaling, self organization and compartmentalization. Obtaining mechanistic insights into their function can therefore be challenging for traditional structural determination techniques. Often, scientists have to rely on piecemeal evidence drawn from diverse experimental techniques to characterize their functional mechanisms. Multiscale simulations can help bridge critical knowledge gaps about IDP structure function relationships - however, these techniques also face challenges in resolving emergent phenomena within IDP conformational ensembles. We posit that scalable statistical inference techniques can effectively integrate information gleaned from multiple experimental techniques as well as from simulations, thus providing access to atomistic details of these emergent phenomena.