Abstract
We address the problem of generating novel molecules with desired interaction properties as a multi-objective optimization problem. Interaction binding models are learned from binding data using graph convolution networks (GCNs). Since the experimentally obtained property scores are recognised as having potentially gross errors, we adopted a robust loss for the model. Combinations of these terms, including drug likeness and synthetic accessibility, are then optimized using reinforcement learning based on a graph convolution policy approach. Some of the molecules generated, while legitimate chemically, can have excellent drug-likeness scores but appear unusual. We provide an example based on the binding potency of small molecules to dopamine transporters. We extend our method successfully to use a multi-objective reward function, in this case for generating novel molecules that bind with dopamine transporters but not with those for norepinephrine. Our method should be generally applicable to the generation in silico of molecules with desirable properties.
Highlights
IntroductionThe in silico (and experimental) generation of molecules or materials with desirable properties is an area of immense current interest (e.g. [1–28])
The in silico generation of molecules or materials with desirable properties is an area of immense current interest (e.g. [1–28])
As stated above we use log transformed values. (We attempted to learn the Ki values of the molecules, but the distribution was found to be heteroscedastic; we focus on predicting the pKi values.) Data are shown in Fig. 4a for the dopamine transporter and 4b for the norepinephrine transporter pKi values
Summary
The in silico (and experimental) generation of molecules or materials with desirable properties is an area of immense current interest (e.g. [1–28]). The in silico (and experimental) generation of molecules or materials with desirable properties is an area of immense current interest Difficulties in producing novel molecules by current generative methods arise because of the discrete nature of chemical space, as well as the large number of molecules [29]. The number of drug-like molecules has been. Earlier approaches to understanding the relationship between molecular structure and properties used methods such as random forests [38, 39], shallow neural networks [40, 41], Support Vector Machines [42], and Genetic Programming [43]. With the recent developments in Deep Learning [44, 45], deep neural networks have come to the fore for property prediction tasks [3, 46–48]. Coley et al [49] used Graph convolutional networks effectively as a feature encoder for input to the neural network.
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