This study presents a data-driven machine learning approach to predict the melting points of organic compounds, leveraging both 2D and 3D molecular descriptors and indicates that ML models can significantly improve melting-point predictions, providing a robust tool for the scientific community.
Abstract
Accurate prediction of melting points for pure molecules remains a significant challenge in predictive chemistry, with implications across various scientific fields, including materials science, drug discovery, and separations chemistry. Traditional methods, such as group contribution (GC) techniques, have shown limited success due to the complex relationship between molecular structure and melting point. In this study, we present a data-driven machine learning (ML) approach to predict the melting points of organic compounds, leveraging both 2D and 3D molecular descriptors. Our results indicate that ML models can significantly improve melting-point predictions, providing a robust tool for the scientific community. Scientific contributionOur detailed analysis on melting point prediction, along with SHAP explainability, reveals the top influencing features for the prediction. The P2MAT application we developed as part of this study can predict both melting and boiling points from a SMILES string. P2MAT is available as an easy-to-install, user-friendly GUI for maximum outreach to the scientific community. Our benchmark analysis demonstrates the excellence of our method for predicting melting points.
Melting point (MP) is an important thermophysical property for the chemical process industry, yet accurate prediction of MP for organic compounds in the absence of experimental data remains challenging due to the complex interplay between molecular packing, intermolecular interactions, and electronic structure. Traditional group contribution and quantitative structure-property relationship models, which rely primarily on static molecular descriptors, often fail to capture these critical condensed-phase effects. In this study, we present a hybrid machine learning framework that integrates cheminformatics descriptors with quantum chemical features and dynamic condensed-phase descriptors derived from molecular dynamics (MD) simulations. Using a curated subset of the DIPPR 801 database, multiple machine learning architectures, including light gradient boosting machine (LightGBM) and graph convolutional networks, were evaluated with feature sets of increasing physical fidelity. The best-performing model, based on LightGBM trained on Dragon descriptors augmented with MD and quantum chemical features, achieves a mean absolute error of 22.5 K, outperforming descriptor-only models and structure-based deep learning baselines. Shapley additive explanations interpretability analysis reveals that melting behavior is governed primarily by molecular topology, surface-area-weighted electronic descriptors, and condensed-phase interaction properties. In contrast, many isolated functional group and single molecule electronic descriptors contribute negligibly once these effects are accounted for. These results demonstrate that incorporating physics-informed, multi-scale descriptors enables more accurate and physically interpretable MP predictions.
Frank T. Mtetwa, N. Giles, W. Wilding et al.· Journal of Chemical Physics· 0 citations
Chemical property prediction plays a critical role in accelerating scientific discovery in chemistry, materials science, and drug development. However, existing benchmarks often suffer from limited task diversity, fragmented datasets, and inconsistent evaluation protocols, making it challenging to systematically assess the reliability and generalization of AI models. In this work, we introduce Chem World, a comprehensive benchmark for chemical property prediction that integrates 17 diverse chemical datasets with over 800,000 molecular samples, covering various properties including density, electrical conductivity, solubility, and other molecular characteristics. Chem World provides a unified platform for evaluating AI models across multiple property prediction tasks. Furthermore, we propose Mixture-PINN, a physics-informed neural network based prediction framework that incorporates chemical prior knowledge into data-driven learning, improving the accuracy, robustness, and reliability of chemical property prediction. Extensive experiments on Chem World demonstrate the effectiveness of our approach compared with existing methods. By combining large-scale standardized evaluation with physics-informed learning, Chem World establishes a foundation for developing trustworthy AI systems for computational chemistry and advancing AI-driven scientific discovery.
Tianyou Bai, Huanfei Wang, Ming Gao et al.· 0 citations
Quantitative prediction of inhibitor potency can accelerate early-stage drug discovery. Recently, data-driven approaches have gained widespread interest in drug discovery, as evidenced by a growing number of benchmarking challenges and open competitions. In this context, we developed a machine learning-based methodology that can find the most effective way of predicting IC50 values against ASK1 from SMILES, for "Jump AI(.py) 2025: 3rd AI Drug Discovery Competition", hosted by the Korea Pharmaceutical and Bio-Pharma Manufacturers Association (KPBMA) on the Dacon platform. Applying our methodology achieved the highest overall predictive performance among all participating teams. Beyond this competition setting, we present a compact SMILES-based modeling workflow comprising (i) a pre-trained encoder, (ii) regression models, (iii) data augmentation, and (iv) hyperparameter tuning. We systematically compared molecular representations from sequence- and graph-based models, including ChemBERTa-2 and MolCLR. Across encoder-regressor combinations, ChemBERTa-77 M-MLM embeddings paired with support vector regression (SVR) yielded the strongest predictive performance. Embedding-level mix-up augmentation and SVR hyperparameter tuning further improved predictive performance. Our findings highlight that careful SMILES preprocessing and encoder selection have a critical influence on IC50 values and provide a reproducible benchmark for single-target bioactivity prediction, thus contributing to a more efficient drug discovery process. Scientific Contribution In this study, we propose a machine learning methodology for predicting the IC50 values of ASK1 inhibitors from SMILES representations, with a systematic comparison of molecular encoders and regression models. Our results show that the use of suitable encoder-regressor pairs together with embedding-level mix-up augmentation improves model generalizability without requiring SMILES-level augmentation. This strategy would be particularly useful for settings with imbalanced labels or limited data, and could be applied more broadly to IC50 prediction for other kinase inhibitors.
Ju Hyung Lee, S. Choi, Utku Ozbulak et al.· Journal of Cheminformatics· 0 citations
The rapid advancement of machine learning (ML)-based protein structure prediction, exemplified by AlphaFold2 and extended by newer models such as AlphaFold3 and Boltz-2, has generated significant optimism for structure-guided drug discovery. In particular, ligand–protein cofolding approaches offer the potential to overcome limitations in generating starting structures for physics-based free energy perturbation (FEP) calculations. However, the practical readiness of ML-predicted structures for FEP applications remains insufficiently evaluated. Here, we systematically assess experimentally determined crystal structures, a homology model, and ML-predicted protein structures as inputs for FEP using a well-characterized congeneric series targeting the tyrosine kinase cSrc. A data set of 133 compounds was evaluated through more than 1400 FEP calculations under minimal optimization to approximate “out-of-the-box” performance. By maintaining consistent preparation protocols, we isolate the impact of structural origin on predictive accuracy. Variable performance was observed across both experimental and ML-predicted structures, highlighting that even under this idealized benchmark scenario, significant challenges remain in reliably generating and refining predictive protein–ligand complexes. This study demonstrates that predictive variation in micro and macro conformational states─rather than the structural source─governs predictive reliability, underscoring the need for careful validation when integrating ML-derived structures into FEP workflows.
Parker Dryja, Morné Muller, Monique Horn et al.· Journal of Chemical Informat...· 0 citations
This review provides a systematic overview of recent advances in SSL-based molecular property prediction and analyzes how multimodal molecular representation learning by integrating sequence, graph, three-dimensional structure, and textual information can improve the quality and expressiveness of molecular representations.
Shuning Yang, Lei Deng· Journal of Chemical Informat...· 0 citations
This tutorial provides a comprehensive, end-to-end workflow from raw data to deployed models,icitly designed for environmental chemists with limited prior experience in ML modeling while also providing practical guidance for other users seeking to strengthen their modeling workflows.
Kai Zhang, Yushu Cheng, Haiping Ai et al.· ACS Environmental Au· 0 citations