Monroe is presented, a new MFM with several innovations over the existing state of the art: increased scale allowing pre-training on over 81 million molecules from the PM6 quantum chemistry dataset; improved graph representation of stereochemistry; improved training losses including conformer denoising and embedding decorrelation; improved multi-task learning; and the use of a prior-data-fitted model (TabPFN) for downstream in-context prediction.
Abstract
Bioassay activity prediction is often data-limited because drug-discovery datasets rely on time-consuming and expensive wet-lab experiments for data generation and evaluation. This challenge has inspired recent research into molecular foundation models (MFMs), which aim to encode general-purpose chemical knowledge into molecular representations that generalize well in data-constrained scenarios. This paper presents Monroe, a new MFM with several innovations over the existing state of the art: increased scale allowing pre-training on over 81 million molecules from the PM6 quantum chemistry dataset; improved graph representation of stereochemistry; improved training losses including conformer denoising and embedding decorrelation; improved multi-task learning; and the use of a prior-data-fitted model (TabPFN) for downstream in-context prediction. Our evaluations use a principled pairwise comparison framework that measures statistically significant performance differences. Across established Polaris benchmarks, Monroe matches or exceeds existing MFMs, while on activity cliff benchmarks, designed to assess utility for molecular discovery, it achieves significant improvements over prior methods. Finally, ablation and transfer experiments show that PFN-based downstream predictors also substantially improve two leading existing models, MiniMol and CheMeleon, yielding new state-of-the-art variants we call MiniMol_PFN and CheMeleon_PFN, suggesting that our downstream adaptation strategy generalizes beyond Monroe. Source code is at github.com/blazejba/monroe.
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
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
Accurate prediction of molecular bioactivity is a fundamental goal in rational drug design but remains challenging due to data scarcity and label imbalance. To address these limitations, we propose a unified Bayesian framework that integrates classification-to-regression knowledge fusion, uncertainty quantification, and active learning for data-efficient molecular property prediction. Specifically, we develop the Bayesian Class-Attentive Transformer Network (BCATNet). This model learns activity patterns from abundant classification data and incorporates the predicted probabilities as informative priors to guide the subsequent Bayesian regression task. Structurally, BCATNet employs a cross-token attention mechanism to model nonlinear interactions between class-derived semantics and molecular structural features. Comparative experiments against conventional machine learning models, graph neural networks, pretrained molecular models, and classification-guided baselines further demonstrated that explicit classification-to-regression knowledge fusion can provide a competitive and data-efficient alternative to generic molecular pretraining. Under reduced regression supervision, BCATNet maintained lower prediction errors and stronger robustness than competing models, supporting its utility in label-scarce settings. Beyond accuracy, the Bayesian formulation generated uncertainty estimates that were informative for reliability assessment: high-uncertainty predictions showed larger regression errors, and uncertainty-based risk stratification separated low-, medium-, and high-risk molecular predictions. Finally, BCATNet uncertainty served as an effective acquisition signal in active learning, with uncertainty-driven strategies achieving the best final performance in most benchmark tasks. Overall, BCATNet establishes a generalizable paradigm for uncertainty-aware molecular modeling by bridging classification and regression tasks within a Bayesian framework, offering a principled route toward reliable, interpretable, and resource-efficient drug discovery.
Shiyang Bian, Yukun Luo, Hongqiao Wang et al.· Journal of Chemical Informat...· 0 citations
This is the first method to expose GNN-derived attributions to an LLM as evidence for property prediction, and achieves the best overall results among generalist models and narrows the gap to specialist models tuned for each task.
Junwoo Park, Minyoung Shin, C. Lee et al.· 0 citations
Multitask learning is a promising strategy in computational drug discovery, potentially improving predictive performance and generalization over traditional single-task models. MTL has shown particular value in absorption, distribution, metabolism, elimination, and toxicity (ADMET) and potency predictions, which are key for drug design. Yet, many existing Web servers rely on the same uncurated, decade-old data sets, creating an illusion of diversity. This work critically reviews open-source ADMET Web services, revealing extensive data redundancy and limited curation across the field. We introduce OneADMET, a meticulously curated data set of 738,161 compounds with 1,119,719 measurements spanning 44 ADMET end points and 1 489 biological activities. We report a unified ChemProp-based MTL model capable of handling hundreds of continuous tasks simultaneously, which has practical advantages for model deployment and maintenance. Additionally, we observed that these MTL models match or surpass single-task models in predictive accuracy. This study highlights the utility of large-scale MTL for pharmacokinetics profiling and contributes practical tools and data sets for the community.
P. Llompart, C. Minoletti, G. Marcou et al.· Journal of Medicinal Chemist...· 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