Trimole-Hybrid is presented, a task-wise multimodal framework that addresses ADMET heterogeneity by selecting or combining predictors built from complementary molecular representations, and shows sensitivity to changes in essential functional motifs, suggesting its ability to capture ADMET-relevant molecular substructures.
Abstract
Motivation Accurate ADMET prediction is essential for prioritizing compounds before costly experimental validation, yet ADMET tasks are highly heterogeneous. Properties such as solubility, permeability, protein binding, clearance, transporter activity and toxicity are governed by different molecular signals, ranging from local functional groups and physicochemical descriptors to bonded topology and three-dimensional geometry. Consequently, a single molecular representation or backbone is unlikely to be optimal across all ADMET tasks. Results We present Trimole-Hybrid, a task-wise multimodal framework that addresses ADMET heterogeneity by selecting or combining predictors built from complementary molecular representations. Trimole-Hybrid constructs a candidate pool of SMILES-, graph-, geometry-sensitive EPT/3D- and chemical descriptor-based predictors. For each task, Trimole-Hybrid selects the best-performing predictor to obtain the final prediction. On 22 Therapeutics Data Commons ADMET benchmarks, Trimole-Hybrid exceeded the public TDC top-1 methods on 10 tasks and ranked within the top 10 for 21 tasks. Ablation studies confirmed the contribution of both complementary multimodal molecular representations and task-specific ensemble strategies. In two small-molecule case studies, Trimole-Hybrid shows sensitivity to changes in essential functional motifs, suggesting its ability to capture ADMET-relevant molecular substructures. Availability and implementation Source code, supplementary tables, and audit files are available at the project repository: https://github.com/dchen0212/trimole_hybrid. Contact liyu@cse.cuhk.edu.hk and qzyu22@cse.cuhk.edu.hk Supplementary information Supplementary data are available with the submitted manuscript.
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
A novel Dual-Attention Multimodal framework for Graphs and Sequence-based representations, so-called DAM-GS, which provides a promising solution for molecular property prediction with broad applications in drug discovery and computational molecular science.
Bay Van Nguyen, Vinh Truong, Ha Duong Thi Hong et al.· Journal of Chemical Informat...· 0 citations
Accurate prediction of drug-target binding affinity (DTA) is a key task in virtual screening. However, current computational methods face a key challenge: sequence-based approaches often fail to capture critical spatial information, while structure-based models rely on computationally expensive 3D coordinates, which restrict their scalability. To address this issue, we propose StructuraDTA, a novel multimodal framework that adopts an implicit structure modeling strategy. Instead of using static protein folding data, our method encodes drug molecular graphs via Graph Isomorphism Networks (GINs) to capture fine-grained topological features. Meanwhile, we optimize protein representations by integrating probabilistic structural priors into a pretrained language model, which effectively simulates thermodynamic conformational flexibility without relying on explicit 3D structural data. A bidirectional cross-attention mechanism is then used to dynamically align these heterogeneous feature modalities. Comprehensive evaluations on the Davis and KIBA benchmark datasets show that StructuraDTA stably outperforms state of-the-art comparison methods. Importantly, the model exhibits strong robustness in cold-start scenarios, and can accurately predict binding affinities for previously unseen drugs and targets. By retaining the predictive performance of structure based models while maintaining the high inference efficiency of sequence-based methods, we provide an accurate and scalable solution to accelerate genome-scale drug discovery research.
Junlin Xu, Ye Yuan, Menglong Hu et al.· IEEE journal of biomedical a...· 0 citations
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
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
Predicting the absorption, distribution, metabolism, excretion and toxicity (ADMET) properties of small molecules remains a major challenge in drug discovery. Here, we present MEGA-CL, a foundation graph neural network framework for universal molecular ADMET prediction. MEGA-CL integrates self-supervised contrastive learning with a multi-head external attention mechanism and an enhanced message-passing architecture, enabling simultaneous modeling of local chemical substructures and global inter-graph relationships while mitigating over-smoothing effects commonly observed in deep graph networks. Across 13 benchmark datasets and 21 downstream ADMET tasks, MEGA-CL consistently outperforms state-of-the-art baseline models. In particular, the framework demonstrates robust performance on challenging regression tasks, including clearance (CL) and steady-state volume of distribution (VDss), while maintaining strong generalization ability in independent external validation. Clinically relevant predictive accuracy was achieved, with more than 75% of predictions falling within a 3-fold error range. In an external evaluation on 18 novel compounds derived from recently approved FDA drugs, over 50% of human liver microsome clearance (HLMC) predictions were within a 2-fold error range. To further assess its practical applicability, MEGA-CL was prospectively evaluated on three preclinical drug candidates using in vitro hepatic microsomal metabolism assays and CYP450 inhibition assays guided by model predictions. The predicted HLMC values for all candidates were within 2.5-fold of the experimentally measured values, and 73.3% of CYP450 inhibition endpoints (11/15) were correctly classified. These results demonstrate the potential of MEGA-CL as a generalizable framework for accelerating in silico ADMET evaluation and early-stage drug candidate optimization.
Tinghui Jin, Kedu Jin, Ying Li et al.· 0 citations