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:2602.00663v3 Announce Type: replace Abstract: Optimizing molecules to achieve desired properties is a central bottleneck across the chemical sciences, particularly in the pharmaceutical industr...
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:2608. 06961v1 Announce Type: new Abstract: Early-stage molecular design is an iterative process, not just a task of generating molecules.
The paper introduces a multitask large reasoning model for molecular science that incorporates chemical knowledge via a multispecialist architecture, chain-of-thought supervision, and molecule-informed reinforcement learning. It coordinates prediction and inference specialists across ten molecular tasks—including description, generation, nomenclature translation, property prediction, and reaction prediction—using task-conditioned routing. The model surpasses more than 20 general-purpose and molecular large language models, improving aggregate performance by 50.3% and outperforming leading multitask baselines on most tasks, while maintaining interpretable chemical inference and demonstrating a workflow for CNS candidate generation and retrosynthetic planning.
arXiv:2607. 19044v1 Announce Type: new Abstract: Leveraging large language models (LLMs) for molecular generation has shown remarkable potential in chemical and drug design.
arXiv:2607. 14512v1 Announce Type: new Abstract: Multi-step retrosynthesis planning seeks to decompose a target molecule into commercially available building blocks through a sequence of feasible reactions.
arXiv:2608. 11483v1 Announce Type: new Abstract: Hit-to-lead optimization requires iterative design of hit analogs across competing potency, selectivity, physicochemical, pharmacokinetic, safety, and synthetic constraints.
arXiv:2608.22967v1 Announce Type: new Abstract: Practical molecular inverse design is rarely a one-shot generation problem; it often takes the form of closed-loop candidate-pool enrichment, where und...
The paper explores how large language models (LLMs) can be trained for small-molecule drug design by using synthetic tasks that are cheaper to evaluate. By employing a curriculum that gradually increases task difficulty, the authors demonstrate that LLMs can learn design strategies that outperform larger models on structure-based lead optimization. This approach shows that scaling post‑training with synthetic tasks can effectively adapt LLMs to high‑cost experimental scenarios that are otherwise infeasible to train on directly.
arXiv:2606. 00008v1 Announce Type: new Abstract: Multi-objective molecular optimization requires searching vast chemical spaces under conflicting objectives, where early design decisions strongly constrain downstream outcomes.
arXiv:2604.07669v3 Announce Type: replace-cross Abstract: Synthesizable molecular optimization seeks to improve target properties while ensuring that molecular modifications follow feasible synthetic...
MolDesignBench is a new benchmark for evaluating large language model (LLM)-based agents in scenario‑grounded molecular design. It contains 2,000 generation and optimization tasks that blend implicit narrative requirements with explicit property and functional‑group constraints, including infeasible cases, and require the use of 17 specialized chemistry tools. Experiments with leading LLMs show low success rates (best ~43%) and highlight failures in implicit‑constraint reasoning, infeasibility detection, and tool usage, underscoring the benchmark’s role in identifying key bottlenecks for future research.