Melting point prediction plays a pivotal role in chemistry, materials science, and pharmaceuticals, where it informs compound identification, purity assessment, and design of thermally robust materials. Despite advances in computational modeling, current methods face challenges in accuracy, interpretability, and computational efficiency, largely due to limited experimental datasets and the nonlinear, discontinuous relationship between molecular structure and melting behavior. In this study, we curate a harmonized dataset of 117 752 unique organic crystals by integrating and standardizing multiple public sources, emphasizing reproducibility and molecular uniqueness. We systematically evaluate classical machine learning models using molecular embeddings derived from SMILES strings, including ChemBERTa, MoLFormer, Morgan fingerprints, and Uni‐Mol, and compare them with advanced graph neural network (GNN) architectures such as our custom implementation and the widely used Chemprop framework. Our analysis demonstrates that gradient‐boosted tree models offer notable computational efficiency, while GNNs and transformer‐based models like MoLFormer provide superior predictive accuracy, particularly when enhanced with attention mechanisms. By integrating ChemBERTa and MoLFormer embeddings in the TabM model, we achieve a mean absolute error (MAE) of 26.75 K and an RMSE of 35.36 K, surpassing individual model performance. These results highlight the benefits of hybrid modeling strategies that balance interpretability, scalability, and accuracy, offering a robust framework for accelerating materials discovery and pharmaceutical development through improved melting point prediction. Nevertheless, Chemprop exhibits considerably higher accuracy but demands approximately five times greater resources during training.
While anti‐human epidermal growth factor receptor 2 (HER2) drugs, including trastuzumab improve the prognosis of patients with HER2‐positive cancers, acquired resistance by cancer cells remains a significant clinical challenge. We had previously reported that YES1 is overexpressed in trastuzumab‐resistant breast and lung cancer cells, and that its inhibition restores trastuzumab sensitivity. However, no approved drug specifically targets YES1. In this study, as a drug repositioning approach aimed at rapid clinical application, we constructed a machine learning model capable of learning the interactions between compounds and target proteins and conducted a screening of approved drugs. The strategy identified chlorpromazine and raloxifene as hit compounds. In vitro validation demonstrated that they effectively inhibited YES1 activation at 15 μM after 48 h. The findings suggested that the identified approved drugs might represent promising therapeutic options for overcoming anti‐HER2 drug resistance.
Machine learning‐driven high‐throughput screening strategy was used for designing efficient nitrogen‐containing small‐molecule acceptors (SMAs) for organic solar cells. First, a database containing 1027 experimentally determined SMAs was encoded using RDKit molecular descriptors with the application of variance filter, correlation filter, and consensus feature selection. Then, 11 different regression algorithms were assessed in terms of PCE prediction; the best results were obtained by CatBoost algorithm. Afterward, a graph‐based nitrogen substitution approach was implemented to design about 200 K chemically valid mono‐, di‐, and tri‐nitrogen‐containing SMA candidates. Further virtual screening allowed us to identify several promising candidates possessing predicted PCE value up to 16.42%. The structural analysis of chemical space revealed that all candidates are diverse and suitable for further investigation.
SMILES2Docking converts spreadsheet-based SMILES libraries into docking-ready 3D ligands through a single, configurable pipeline. Salt and coformer removal, a three-mode stereocenter policy, prediction of the dominant protonation state with a graph neural network pKa model (MolGpKa) refined by iterative titration, distance-geometry embedding with RDKit, a molecular-mechanics optimization cascade, and an optional semi-empirical refinement under an implicit solvation model are combined in one tool, with records processed either sequentially or distributed across CPU cores. Users select among permissive, strict, and enumerative stereocenter modes, so the same tool serves broad exploratory screening and precision docking. A per-compound JSON audit report records the ionization and stereocenter decisions taken for every structure. The tool is distributed as a Python package, a Windows executable with bundled MOPAC, a Linux portable bundle, and a macOS bundle, under GPL-2.0-or-later, and is freely available to non-commercial users at https://github.com/amgoncalvesusp/Smiles2Docking (DOI: 10.5281/zenodo.20617898).
Medicinal-chemistry review is often built from disconnected notebooks, spreadsheets, and slide decks that hide structure-normalization choices and blur the boundary between measured observations and heuristic prioritization. MolScope is an open-source Python workflow toolkit that converts molecular structure collections and optional assay joins into a canonical chemistry table, category-aware summaries, and decision-ready report and picklist artifacts. The workflow makes representation policy explicit, preserves evidence provenance, and packages outputs as portable HTML, Markdown, and CSV bundles. Using frozen example datasets, we show reproducible end-to-end execution, auditable handling of salts, tautomers, stereo ambiguity, charge state, and one bounded round-review example for comparative campaign support.
