The paper introduces Mol-E, an evolutionary algorithm that leverages large language models trained on molecular data to generate candidate molecules. Mol-E achieves state‑of‑the‑art performance on the Practical Molecular Optimization benchmark, scoring 17.500 in the task‑agnostic regime and 20.551 in the task‑informed regime. It also outperforms baseline methods in multi‑property optimization tasks involving docking against DRD2, MK2, and AChE.
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:2604.07669v3 Announce Type: replace-cross Abstract: Synthesizable molecular optimization seeks to improve target properties while ensuring that molecular modifications follow feasible synthetic...
arXiv:2607. 12488v1 Announce Type: new Abstract: Molecular optimization in drug discovery, materials design, and catalysis requires searching vast chemical spaces under tight evaluation budgets, since high-fidelity oracles and experimental measurements are costly.
PMO-Dock introduces a standardized benchmark for docking‑based molecular optimization, comprising 25 tasks that span hit generation, lead optimization, and a specificity task that rewards strong on‑target binding while penalizing off‑target interactions. The protocol separates model development from final evaluation, using validation tasks for hyperparameter tuning and held‑out test tasks evaluated under a strict oracle budget to mimic realistic drug‑discovery constraints. Four diverse methods—Saturn, GenMol, Genetic‑guided GFlowNet, and ChemLlama—were benchmarked, revealing no single method dominates across all tasks and highlighting limited hyperparameter transferability, which underscores the need for method‑dependent selection strategies.
arXiv:2607. 19044v1 Announce Type: new Abstract: Leveraging large language models (LLMs) for molecular generation has shown remarkable potential in chemical and drug design.
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:2606. 11256v1 Announce Type: cross Abstract: Designing molecules with target properties is most useful when candidate structures are accompanied by feasible synthetic routes.
arXiv:2607. 28437v1 Announce Type: new Abstract: Molecular optimization is commonly performed under a limited oracle budget, which makes deciding what to evaluate as important as deciding what to generate.
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: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: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: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.