A self-attention based message passing neural network for predicting molecular lipophilicity and aqueous solubility

Abstract

Efficient and accurate prediction of molecular properties, such as lipophilicity and solubility, is highly desirable for rational compound design in chemical and pharmaceutical industries. To this end, we build and apply a graph-neuralnetwork framework called self-attention-based message-passing neural network (SAMPN) to study the relationship between chemical properties and structures in an interpretable way. The main advantages of SAMPN are that it directly uses chemical graphs and breaks the black-box mold of many machine/deep learning methods. Specifically, its attention mechanism indicates the degree to which each atom of the molecule contributes to the property of interest, and these results are easily visualized. Further, SAMPN outperforms random forests and the deep learning framework MPN from Deepchem. In addition, another formulation of SAMPN (Multi-SAMPN) can simultaneously predict multiple chemical properties with higher accuracy and efficiency than other models that predict one specific chemical property. Moreover, SAMPN can generate chemically visible and interpretable results, which can help researchers discover new pharmaceuticals and materials. The source code of the SAMPN prediction pipeline is freely available at Github (https ://githu b.com/tbwxm u/SAMPN ).

Inductive Transfer Learning for Molecular Activity Prediction: Next-Gen QSAR Models with MolPMoFiT

“…QSPR/QSAR case studies. In addition to use expert-engineered molecular descriptors as input, those techniques can also directly take molecular structures (e.g., molecular graph [10][11][12][13][14][15][16][17][18][19] [20,21], SMILES strings [22][23][24], and molecular 2D/3D grid image [25][26][27][28][29][30]) and learn the data-driven feature representations for predicting properties/activities. As a result, this type of approach is potentially able to capture and extract underlying, complex structural patterns and feature  property relationships given sufficient amount of training data.…”

Could Graph Neural Networks Learn Better Molecular Representation for Drug Discovery? A Comparison Study of Descriptor-based and Graph-based Models

“…Many recently proposed GNN architectures for molecular property prediction can be formulated in this flexible framework. [25,27,35,38] In theory, MPNN operates the convolutions on undirected molecular graphs G = (V, E) with node features X v and edge features E km . The forward propagation of MPNN has two phases: message passing phase and readout phase.…”

“…In most of these reported studies, traditional ML models such as LR, RF, SVM (especially 'gold standard' RF) [32,38] were employed to develop the prediction models based on a set of individual fingerprints (especially Extended Connectivity Fingerprints, ECFP) [32][33][34]. However, it is well known that the performance of descriptor-based models is highly depending on the descriptors used in training and many previous studies have highlighted that ML models only based on molecular fingerprints are not such well-performing [5,6,39,40].…”

AI-aided design of novel targeted covalent inhibitors against SARS-CoV-2

“…Thus, with the hope to discover novel candidate drugs targeting SARS-CoV-2, we combine artificial intelligence (AI) with the structure-based drug design (SBDD) to accelerate generating potential lead compounds and design TCIs. AI, especially deep learning, has been applied in predicting molecular properties 30,31,32,33 and designing novel molecules 34 . Unlike earlier deep-learning molecular design by adding single atom one at a time 34,35 , our approach explores new molecules by adding a meaningful molecular fragment one by one, which is not only computationally more efficient but also chemically more reasonable.…”