MoLRSI is a recursive self‑improving AI agent designed for molecular discovery that optimizes both workflow and model architecture using persistent cross‑task memory. It refines its search policy based on workflow performance, refinement outcomes, and execution feedback, enabling continuous improvement. In antibiotic discovery, MoLRSI achieved a 15.9% experimental hit rate versus 8.7% for an expert‑led workflow, and in de novo peptide‑MHC binder design it produced eight validated hits with preferential binding, outperforming a baseline approach.
arXiv:2608. 06961v1 Announce Type: new Abstract: Early-stage molecular design is an iterative process, not just a task of generating molecules.
arXiv:2610.23791v1 Announce Type: new Abstract: Computational drug discovery requires agents to compose scientific operations and adapt to changing evidence and constraints. Here we introduce DrugHar...
arXiv:2502. 18864v2 Announce Type: replace Abstract: Scientific discovery is driven by scientists generating novel hypotheses for complex problems that undergo rigorous experimental validation.
arXiv:2610.16975v1 Announce Type: new Abstract: Drug discovery remains a resource-intensive and time-consuming process, involving billions of dollars and a decade of development to advance a drug to...
arXiv:2608. 02642v1 Announce Type: cross Abstract: Accelerating scientific discovery is among the most consequential applications of AI, and computational biomolecular simulation stands out as a particularly promising target within this broader effort.
The paper introduces MAGI, an open modular agent that orchestrates molecular‑design tools by setting objectives, launching optimization, interpreting structure–activity relationships, and adjusting strategy. In nine retrospective lead‑optimization campaigns from three pharmaceutical companies, MAGI produced valid molecules via either direct LLM generation or delegation to REINVENT 4; LLM proposals stayed closer to local chemistry and achieved comparable or higher activity with fewer operations, while REINVENT explored broader chemical space. The success of a campaign depended on the accuracy of predictive models rather than the generation route, and a blinded evaluation showed chemists could not distinguish MAGI’s outputs from expert‑generated compounds, though the SAR reasoning was deemed plausible yet incomplete. The study positions MAGI as a coordination layer that can be plugged into existing workflows, but notes that the current ceiling for real projects is set by scorer applicability rather than tool orchestration.
arXiv:2610.19920v1 Announce Type: new Abstract: Molecular design is often formulated as an optimization problem over existing molecular spaces. However, molecular optimization and the discovery of st...
M3OS is a multi‑agent large‑language‑model system that separates molecular‑design reasoning from optimization‑state management using a Monte Carlo graph search. The system maintains a persistent graph of evaluated candidates, transformations, and evidence, while LLM agents use role‑specific contexts to generate and edit molecules with tool‑driven and knowledge‑guided approaches. Across three benchmarks, M3OS outperforms baselines, demonstrating the benefit of persistent search state, specialized agents, and controlled execution for multi‑constraint molecular optimization.
arXiv:2606. 11150v1 Announce Type: new Abstract: Large language models (LLMs) are rapidly acquiring capabilities relevant to biological research, from literature synthesis to interpretation of experimental data.
arXiv:2606. 11256v1 Announce Type: cross Abstract: Designing molecules with target properties is most useful when candidate structures are accompanied by feasible synthetic routes.
arXiv:2609.05818v1 Announce Type: new Abstract: We introduce ABLE, a benchmark for evaluating LLM agents' ability to use biological AI models (BAIMs), such as ProteinMPNN and AlphaFold3, in dual-use...
arXiv:2511.20510v3 Announce Type: replace Abstract: Molecule generation from extremely limited training data is a key challenge in drug discovery. Existing fragment-based methods are more suitable th...