Chem World is introduced, 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 and Mixture-PINN is proposed, a physics-informed neural network based prediction framework that incorporates chemical prior knowledge into data-driven learning.
Abstract
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.
Experiments show that PWAV generally improves over classical fingerprint descriptors within learned models and achieves competitive performance relative to established external baselines on several endpoints, positioning PWAV as a competitive and chemically transparent component for hybrid molecular property prediction, rather than as a replacement for domain-specific benchmark systems.
M. Afzal, S. Siddiqi· Physica Scripta· 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
CoMPASS is presented, a retrieval-calibrated framework for small-large model collaboration that retains a graph attention network as the predictive anchor, retrieves locally relevant training molecules, provides attention-grounded evidence to an LLM, and converts its proposal into a bounded correction through an agreement-aware gate.
Wen-Tao Li, Jiang-Jie Qiu, Yi-Jun Li et al.· 0 citations
Molecular property prediction is a key task in AI-driven drug discovery, yet the prevalence and impact of label imbalances in molecular property regression remain poorly understood. Through a systematic benchmark of widely used molecular property data sets, we show that target values are often highly imbalanced and that prediction errors are consistently concentrated in sparsely represented regions of the label space. We further evaluate representative imbalance-learning approaches developed for general regression tasks and find that their effectiveness on molecular data sets is limited. To address this challenge, we propose interval-aware mixture-of-experts (IA-MoE), a plug-in framework that partitions the continuous target space into intervals and promotes expert specialization across different regions of the label distribution. IA-MoE can be seamlessly integrated with diverse molecular encoders without modifying their underlying architectures. Experiments on five molecular property benchmarks and four graph neural network backbones demonstrate that IA-MoE consistently improves the predictive performance in underrepresented regions while maintaining or improving overall accuracy. Our findings establish label imbalance as an important challenge in molecular property regression and highlight interval-aware expert learning as an effective strategy for addressing this challenge.
Unknown authors· Journal of Chemical Informat...· 0 citations
This work addresses one of the most pervasive obstacles to applying AI in real-world drug development by addressing conformal prediction framework tailored to label shift by weighting conformal scores using marginal label probability ratios and enhancing the trustworthiness of AI-driven predictions.
Hyeonsu Lee, Juyeong Kim, Erkhembayar Jadamba et al.· 0 citations
This work proposes pretraining on low-noise, calculable molecular descriptors via supervised learning to obtain rich, highly transferable molecular representations and demonstrates this strategy with CheMeleon, a O(10M) parameter foundation model that enables directed message-passing neural networks to finally exceed the performance of classical methods in the low-data regime.
Jackson W. Burns, Akshat Shirish Zalte, C. Abreu et al.· Journal of Chemical Informat...· 0 citations
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