We explore a "best-of-both" approach to modelling molecular properties by combining learned molecular descriptors from a graph neural network (GNN) with general-purpose descriptors and a mixed ensemble of machine learning (ML) models. We introduce a MetaModel framework to aggregate predictions from a diverse set of leading ML models. We present a featurisation scheme for combining task-specific GNN-derived features with conventional molecular descriptors. We demonstrate that our framework outperforms the cutting-edge ChemProp model on all regression datasets tested and 6 of 9 classification datasets. We further show that including the GNN features derived from ChemProp boosts the ensemble model's performance on several datasets where it otherwise would have underperformed. We conclude that to achieve optimal performance across a wide set of problems, it is vital to combine general-purpose descriptors with task-specific learned features and use a diverse set of ML models to make the predictions.

Chemical pretrained models, sometimes referred to as foundation models, are receiving considerable interest for drug discovery applications. The general chemical knowledge extracted from self-supervised training has the potential to improve predictions for critical drug discovery endpoints, including on-target potency and ADMET properties. Multi-task learning has previously been successfully leveraged to improve predictive models. Here, we show that enabling multitasking in finetuning of chemical pretrained graph neural network models such as Kinetic GROVER Multi-Task (KERMT), an enhanced version of the GROVER model, and Knowledge-guided Pre-training of Graph Transformer (KGPT) significantly improves performance over non-pretrained graph neural network models. Surprisingly, we find that the performance improvement from finetuning KERMT in a multitask manner is most significant at larger data sizes. Additionally, we publish two multitask ADMET data splits to enable more accurate benchmarking of multitask deep learning methods for drug property prediction. Finally, we provide an accelerated implementation of the KERMT model on GitHub, unlocking large-scale pretraining, finetuning, and inference in industrial drug discovery workflows.

A strong and fast baseline in molecular property prediction is a Random Forest (RF) trained on ECFP4/ECFP6 descriptors. In practice, the count-based variant of ECFP generally outperforms the binary variant, especially for classification. Recent deep-learning approaches can match or exceed these baselines, including pretrained transformer-CNN models (5) and graph neural networks such as ChemProp or AttentiveFP(6). Chemprop's key architectural choice is directed, bond-centered message passing, in contrast to the more common atom-centered formulations used by many MPNNs. Because much of the remaining architecture is comparable across message-passing GNNs, this raises a focused question: what concrete advantage does the bond-centered formulation confer over atom-centered approaches? To isolate this representational factor, we introduce a static Bond-Centered Fingerprint (BCFP) that mirrors Chemprop's bond-centric view, and we compare it directly against ECFP using a lightweight Random Forest or XGBoost pipeline on the Blood-Brain Barrier Penetration (BBBP) classification task. To our knowledge, this is the first study to propose BCFP and analyze its complementarity with ECFP (7) . Our results indicate that concatenating atom-and bond-centered fingerprints yields efficient and effective models for BBBP prediction, clarifying why bond-centric message passing often appears among top-k performers while offering a simple, fast alternative to full neural architectures.

Fast and accurate prediction of molecular properties with machine learning is pivotal to scientific advancements across myriad domains. Foundation models in particular have proven especially effective, enabling accurate training on small, real-world datasets. This study introduces CheMeleon, a novel molecular foundation model pre-trained on deterministic molecular descriptors from the Mordred package, leveraging a Directed Message-Passing Neural Network to predict these descriptors in a noise-free setting. Unlike conventional approaches relying on noisy experimental data or biased quantum mechanical simulations, CheMeleon uses low-noise molecular descriptors to learn rich molecular representations. Evaluated on 58 benchmark datasets from Polaris and MoleculeACE, CheMeleon achieves a win rate of 79% on Polaris tasks, outperforming baselines like Random Forest (46%), fastprop (39%), and Chemprop (36%), and a 97% win rate on MoleculeACE assays, surpassing Random Forest (63%) and other foundation models. However, it struggles to distinguish activity cliffs like many of the tested models. The t-SNE projection of CheMeleon's learned representations demonstrates effective separation of chemical series, highlighting its ability to capture structural nuances. These results underscore the potential of descriptor-based pre-training for scalable and effective molecular property prediction, opening avenues for further exploration of descriptor sets and unlabeled datasets.

At the beginning of the year, I have a feeling that Graph Neural Nets (GNNs) became a buzzword. As a researcher in this field, I feel a little bit proud (at least not ashamed) to say that I work on this. It was not always the case: three years ago when I was talking to my peers, who got busy working on GANs and Transformers, the general impression that they got on me was that I was working on exotic niche problems. Well, the field has matured substantially and here I propose to have a look at the top applications of GNNs that we have recently had. If this in-depth educational content on graph neural networks is useful for you, you can subscribe to our AI research mailing list to be alerted when we release new material.

Graph neural networks have become very popular for machine learning on molecules due to the expressive power of their learnt representations. However, molecular machine learning is a classically low-data regime and it isn't clear that graph neural networks can avoid overfitting in low-resource settings. In contrast, fingerprint methods are the traditional standard for low-data environments due to their reduced number of parameters and manually engineered features. In this work, we investigate whether graph neural networks are competitive in small data settings compared to the parametrically 'cheaper' alternative of fingerprint methods. When we find that they are not, we explore pretraining and the meta-learning method MAML (and variants FO-MAML and ANIL) for improving graph neural network performance by transfer learning from related tasks. We find that MAML and FO-MAML do enable the graph neural network to outperform models based on fingerprints, providing a path to using graph neural networks even in settings with severely restricted data availability. In contrast to previous work, we find ANIL performs worse that other meta-learning approaches in this molecule setting. Our results suggest two reasons: molecular machine learning tasks may require significant task-specific adaptation, and distribution shifts in test tasks relative to train tasks may contribute to worse ANIL performance.

One hundred years ago, the state of the art in finding antibiotics was epitomized by the playful explorations of Alexander Fleming, the Scotsman who discovered penicillin. "I play with microbes," Fleming is quoted as having said. "It is very pleasant to break the rules and to be able to find something nobody had thought of." Today's research in antibiotics is conducted somewhat more mechanically, perhaps, but it's still important to break the rules sometimes, to look where one might not otherwise. Scientists at the Massachusetts Institute of Technology and Harvard last month described in the scholarly journal Cell how they used a deep learning neural network to identify a molecular compound that's different from most antibiotics.