ReMedI introduces an inference‑time refinement framework that combines a pre‑trained molecular language model with a Metropolis‑Hastings search to perform multi‑objective optimization of drug leads. The method proposes edits conditioned on the current molecule and refines them to improve properties, preserve lead similarity, and maintain scaffold integrity. Experiments on C‑MuMOInstruct and SMDD benchmarks show that ReMedI outperforms RL‑based baselines, achieving higher success rates and requiring far fewer training leads.
arXiv:2609.00189v1 Announce Type: new Abstract: Goal-directed optimization is essential for steering molecular generators to propose candidates with desired properties. However, it is often implement...
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:2610.18851v1 Announce Type: new Abstract: Multi-objective molecular optimization is a critical process in drug discovery that aims to modify a given molecule to simultaneously improve multiple...
arXiv:2610.19123v1 Announce Type: new Abstract: Generating novel molecules with desired properties is a central challenge in drug discovery. Although foundational work focused on de novo generation,...
arXiv:2509. 26405v2 Announce Type: replace Abstract: We introduce InVirtuoGen, a discrete flow generative model for fragmented SMILES for de novo and fragment-constrained generation, and target-property/lead optimization of small molecules.
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:2609.36683v1 Announce Type: new Abstract: Molecular optimization is inherently iterative: a candidate is proposed, evaluated against several objectives, and revised while preserving a relations...
arXiv:2607. 02834v1 Announce Type: new Abstract: Molecular optimization often starts from a pretrained generative model that captures a broad prior over valid molecular structures.
The paper introduces LoopMol, a closed‑loop molecular inverse‑design framework that uses a large language model (LLM) as a surrogate to reason directly over task instructions, SMILES optimization histories, and oracle feedback. At each iteration, the LLM selects informative reference molecules and optionally a concise guidance sentence, conditioning the next generation round and producing an inspectable optimization trace. Experiments on six drug and material tasks across three Llama‑based backbones show that the LLM‑based variant outperforms one‑shot generation and competes with or surpasses Gaussian‑process Bayesian optimization, with performance varying by task type.
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:2606. 18961v1 Announce Type: new Abstract: Protein language models (PLMs) have emerged as powerful tools for controllable biomolecular design, yet their post-training adaptation typically relies on costly wet-lab validation or curated preference datasets.