arXiv AI
SEISMO: Explanation-Aware, Trajectory-Conditioned LLM Agents for Sample-Efficient Molecular Optimisation
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv Computation and Language
Aug 25
Closed-Loop Bayesian Molecular Inverse Design with Semantic LLM Surrogates
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...
arXiv Machine Learning
Jun 11
My Chemical Harness: Evolutionary Molecular Design over Synthetic Pathways with Large Language Model Agents
arXiv:2606. 11256v1 Announce Type: cross Abstract: Designing molecules with target properties is most useful when candidate structures are accompanied by feasible synthetic routes.
Hugging Face Trending Papers
Sep 28
M3OS: A Monte Carlo Graph Search-Orchestrated Multi-Agent LLM System for Evidence-Traced Molecular Optimization
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 AI
Sep 16
LLM-Guided Dynamic Action Spaces for Synthesizable Molecular Optimization
arXiv:2604.07669v3 Announce Type: replace-cross Abstract: Synthesizable molecular optimization seeks to improve target properties while ensuring that molecular modifications follow feasible synthetic...
arXiv Machine Learning
Jul 22
Adopting Reinforcement Learning with Verifiable Rewards for Molecular Generation
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 AI
Jun 2
AgentPLM: Agentic Protein Language Models with Reasoning-Augmented Decoding for Protein Sequence Design
arXiv:2606. 02386v1 Announce Type: new Abstract: Protein language models (PLMs) are passive oracles: they generate sequences in a single forward pass with no mechanism to consult external biophysical feedback or redirect generation when a candidate violates thermodynamic or structural constraints.