How to produce expressive molecular representations is a fundamental challenge in artificial intelligence-driven drug discovery. Graph neural network (GNN) has emerged as a powerful technique for modeling molecular data. However, previous supervised approaches usually suffer from the scarcity of labeled data and poor generalization capability. Here, we propose a novel molecular pre-training graph-based deep learning framework, named MPG, that learns molecular representations from large-scale unlabeled molecules. In MPG, we proposed a powerful GNN for modelling molecular graph named MolGNet, and designed an effective self-supervised strategy for pre-training the model at both the node and graph-level. After pre-training on 11 million unlabeled molecules, we revealed that MolGNet can capture valuable chemical insights to produce interpretable representation. The pre-trained MolGNet can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of drug discovery tasks, including molecular properties prediction, drug-drug interaction and drug-target interaction, on 14 benchmark datasets. The pre-trained MolGNet in MPG has the potential to become an advanced molecular encoder in the drug discovery pipeline.

Modeling the impact of amino acid mutations on protein-protein interaction plays a crucial role in protein engineering and drug design. In this study, we develop GeoPPI, a novel structure-based deep-learning framework to predict the change of binding affinity upon mutations. Based on the three-dimensional structure of a protein, GeoPPI first learns a geometric representation that encodes topology features of the protein structure via a self-supervised learning scheme. These representations are then used as features for training gradient-boosting trees to predict the changes of protein-protein binding affinity upon mutations. We find that GeoPPI is able to learn meaningful features that characterize interactions between atoms in protein structures. In addition, through extensive experiments, we show that GeoPPI achieves new state-of-the-art performance in predicting the binding affinity changes upon both single- and multi-point mutations on six benchmark datasets. Moreover, we show that GeoPPI can accurately estimate the difference of binding affinities between a few recently identified SARS-CoV-2 antibodies and the receptor-binding domain (RBD) of the S protein. These results demonstrate the potential of GeoPPI as a powerful and useful computational tool in protein design and engineering. Our code and datasets are available at: https://github.com/Liuxg16/GeoPPI.

Hydrogen peroxide (H2O2)-generating enzymes (HGEs) are potentially useful for tumor therapy, but the potential is limited by the challenge in regulating H2O2 production. Herein, we present site-specific in situ growth of a cationic polymer poly­(N,N′-dimethylamino-2-ethyl methacrylate) (PDMA) from the N-terminus of glucose oxidase (GOX) to generate a site-specific and cationic GOX–PDMA conjugate with well-retained activity and enhanced stability to regulate H2O2 generation for cancer starvation and H2O2 therapy. Notably, the efficiency of endocytosis of the conjugate was 4-fold higher than that of free GOX. As a result, relative to free GOX, the conjugate showed 1.5-fold increased cytotoxicity, 2-fold enhanced tumor retention, and 5-fold increased tolerability after intratumoral injection. Importantly, a single intratumoral injection of the conjugate completely abolished colon tumors without detectable side effects, whereas free GOX was ineffective and systemically toxic. This chemistry may provide a new, simple, general, and efficient solution to regulate H2O2 production and thereby to dramatically improve the antitumor efficacy of HGEs while reducing side effects.

Motivation Computational methods accelerate drug discovery and play an important role in biomedicine, such as molecular property prediction and compound–protein interaction (CPI) identification. A key challenge is to learn useful molecular representation. In the early years, molecular properties are mainly calculated by quantum mechanics or predicted by traditional machine learning methods, which requires expert knowledge and is often labor-intensive. Nowadays, graph neural networks have received significant attention because of the powerful ability to learn representation from graph data. Nevertheless, current graph-based methods have some limitations that need to be addressed, such as large-scale parameters and insufficient bond information extraction. Results In this study, we proposed a graph-based approach and employed a novel triplet message mechanism to learn molecular representation efficiently, named triplet message networks (TrimNet). We show that TrimNet can accurately complete multiple molecular representation learning tasks with significant parameter reduction, including the quantum properties, bioactivity, physiology and CPI prediction. In the experiments, TrimNet outperforms the previous state-of-the-art method by a significant margin on various datasets. Besides the few parameters and high prediction accuracy, TrimNet could focus on the atoms essential to the target properties, providing a clear interpretation of the prediction tasks. These advantages have established TrimNet as a powerful and useful computational tool in solving the challenging problem of molecular representation learning. Availability The quantum and drug datasets are available on the website of MoleculeNet: http://moleculenet.ai. The source code is available in GitHub: https://github.com/yvquanli/trimnet. Contact xjyao@lzu.edu.cn, songsen@tsinghua.edu.cn

Self-assembly of site-selective protein−polymer conjugates into stimuli-responsive micelles is interesting owing to their potential biomedical applications, ranging from molecular imaging to drug delivery, but remains a significant challenge. Herein we report a method of siteselective in situ growth-induced self-assembly (SIGS) to synthesize site-specific human serum albumin-poly(2-(diisopropylamino)ethyl methacrylate) (HSA-PDPA) conjugates that can in situ self-assemble into pH-responsive micelles with tunable morphologies. Indocyanine green (ICG) was selectively loaded into the core of sphere-like HSA-PDPA micelles to form pH-responsive fluorescence nanoprobes. The nanoprobes rapidly dissociated into protonated individual unimers at a transition pH of around 6.5, that is the extracellular pH of tumors, which resulted in a sharp fluorescence increase and markedly enhanced cellular uptake. In a tumor-bearing mouse model, they exhibited greatly enhanced tumor fluorescence imaging as compared to ICG alone and pH-nonresponsive nanoprobes. These findings suggest that pH-responsive and site-selective protein−polymer conjugate micelles synthesized by SIGS are promising as a new class of tumor microenvironment-responsive nanocarriers for enhanced tumor imaging and therapy.

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How to produce expressive molecular representations is a fundamental challenge in AIdriven drug discovery. Graph neural network (GNN) has emerged as a powerful technique for modeling molecular data. However, previous supervised approaches usually suffer from the scarcity of labeled data and have poor generalization capability. Here, we proposed a novel Molecular Pre-training Graph-based deep learning framework, named MPG, that leans molecular representations from large-scale unlabeled molecules. In MPG, we proposed a powerful MolGNet model and an effective self-supervised strategy for pre-training the model at both the node and graph-level. After pre-training on 11 million unlabeled molecules, we revealed that MolGNet can capture valuable chemistry insights to produce interpretable representation. The pre-trained MolGNet can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of drug discovery tasks, including molecular properties prediction, drug-drug interaction, and drug-target interaction, involving 13 benchmark datasets. Our work demonstrates that MPG is promising to become a novel approach in the drug discovery pipeline.