The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters. Abstract. The task of learning an expressive molecular representation is central to developing quantitative structureactivity and property relationships. Traditional approaches rely on group additivity rules, empirical measurements or parameters, or generation of thousands of descriptors. In this paper, we employ a convolutional neural network for this embedding task by treating molecules as undirected graphs with attributed nodes and edges. Simple atom and bond attributes are used to construct atom-specific feature vectors that take into account the local chemical environment using different neighborhood radii. By working directly with the full molecular graph, there is a greater opportunity for models to identify important features relevant to a prediction task. Unlike other graph-based approaches, our atom featurization preserves molecule-level spatial information that significantly enhances model performance. Our models learn to identify important features of atom clusters for the prediction of aqueous solubility, octanol solubility, melting point, and toxicity. Extensions and limitations of this strategy are discussed. the molecular representation. The importance of any one substructure over another is not presupposed, but multiple substructures are represented by the same fingerprint index. (ii) Determination of empirical solute descriptors. Linear free energy relationships are examples of this approach. 4 In the case of Abraham relationships, each solute is described by five empirical parameters, corresponding to different chemical functionalities; solvents are similarly parameterized. The free energy of solvation or any derivative property is then written as a combination of solute-solvent interactions. (iii) Concatenation of many known molecular descriptors, which may include calculation of electronic descriptors or the use of existing property-estimation models. There are many software packages designed to calculate hundreds or even thousands of descriptors, including Dragon 5 and CDK 6 among others. The majority of recent QSAR studies rely on this approach. (iv) Less commonly, SMILES strings or InChI strings, which are two different methods to encode molecules as a character sequence can be directly used directly as model inputs. [7] [8] [9] [10] As expected, initial approaches to the second half of the prediction task, , involved simple regressions (e.g., linear, quadratic) 11 ; due to the high dimensionality of feature vectors, individual features with little effect on the final prediction are often downselected to produce a lower-dimensional, less-parameterized regression. 1, 12 These regression techniques progressed into more advanced techniques as machine learning grew in popularity, including the use of support vector machines, random forest models, Bayesian networks, and artificial neural networks. The application of machine learning to QSAR regression problems has been the subject of several studies and reviews. [13] [14] [15] [16] [17] Despite the application of machine learning approaches to , insufficient attention has been paid to developing flexible approaches to the first step -the embedding strategy that produces a molecular feature vector. This step may offer more room for improvement than the regression technique alone. Multiple important substructures can "collide" into the same fingerprint index in an ECFP 3 , while other indices may be irrelevant to the prediction task, because there is no flexibility in index assignment. Empirical descriptors either require experimental data, a prohibitive requirement for virtual high throughput screening, or a separate QSPR model to predict those descriptors. Descriptor-based models rely on the assumption that all information relevant to the prediction task is included in the chosen descriptor set, limiting the opportunity for a model to learn beyond current chemical expertise. Learning a proper molecular representation, , while learning its relation to the prediction target, , can lead to superior performance over fixed-representation models. Lusci and Baldi describe recurrent neural network models that treat molecules as sets of undirected, acyclic graphs after performing suitable disconnections. 18 Duvenaud et al. reported a convolutional neural network as a differentiable (i.e., trainable) alternative to circular fingerprints. 19 More recently, Kearnes et al. reported an alternate graph convolution approach for prediction of outcomes in various biological assays. 20 Each of these three approaches use only simple structural attributes calculable based on the immediate neighborhood around an atom with the exception of Kearnes et al., who include an estimate of partial charge. Because the only spatial information available is graph distance, information more closely related to 3D geometric distances (e.g., whether an atom is exposed enough to participate in solute-solvent interactions) is lost. The authors would
More recently, Coley et al. introduced a convolutional approach for attributed molecular graphs that learns graph embeddings REF as opposed to node embeddings.
Convolutional Embedding of Attributed Molecular Graphs for Physical Property Prediction.
Journal of chemical information and modeling
Journal of chemical information and modeling
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