Here, we present the outcomes from the second Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry, which engaged participants across global hybrid locations, resulting in 34 team submissions. The submissions spanned seven key application areas and demonstrated the diverse utility of LLMs for applications in (1) molecular and material property prediction; (2) molecular and material design; (3) automation and novel interfaces; (4) scientific communication and education; (5) research data management and automation; (6) hypothesis generation and evaluation; and (7) knowledge extraction and reasoning from scientific literature. Each team submission is presented in a summary table with links to the code and as brief papers in the appendix. Beyond team results, we discuss the hackathon event and its hybrid format, which included physical hubs in Toronto, Montreal, San Francisco, Berlin, Lausanne, and Tokyo, alongside a global online hub to enable local and virtual collaboration. Overall, the event highlighted significant improvements in LLM capabilities since the previous year's hackathon, suggesting continued expansion of LLMs for applications in materials science and chemistry research. These outcomes demonstrate the dual utility of LLMs as both multipurpose models for diverse machine learning tasks and platforms for rapid prototyping custom applications in scientific research.

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.