The drug discovery process can be significantly improved by applying deep reinforcement learning (RL) methods that learn to generate compounds with desired pharmacological properties. Nevertheless, RL-based methods typically condense the evaluation of sampled compounds into a single scalar value, making it difficult for the generative agent to learn the optimal policy. This work combines self-attention mechanisms and RL to generate promising molecules. The idea is to evaluate the relative significance of each atom and functional group in their interaction with the target, and to utilize this information for optimizing the Generator. Therefore, the framework for de novo drug design is composed of a Generator that samples new compounds combined with a Transformer-encoder and a biological affinity Predictor that evaluate the generated structures. Moreover, it takes the advantage of the knowledge encapsulated in the Transformer's attention weights to evaluate each token individually. We compared the performance of two output prediction strategies for the Transformer: standard and masked language model (MLM). The results show that the MLM Transformer is more effective in optimizing the Generator compared with the state-of-the-art works. Additionally, the evaluation models identified the most important regions of each molecule for the biological interaction with the target. As a case study, we generated synthesizable hit compounds that can be putative inhibitors of the enzyme ubiquitin-specific protein 7 (USP7).

The generation of candidate hit molecules with the potential to be used in cancer treatment is a challenging task. In this context, computational methods based on deep learning have been employed to improve in silico drug design methodologies. Nonetheless, the applied strategies have focused solely on the chemical aspect of the generation of compounds, disregarding the likely biological consequences for the organism's dynamics. Herein, we propose a method to implement targeted molecular generation that employs biological information, namely, disease-associated gene expression data, to conduct the process of identifying interesting hits. When applied to the generation of USP7 putative inhibitors, the framework managed to generate promising compounds, with more than 90% of them containing drug-like properties and essential active groups for the interaction with the target. Hence, this work provides a novel and reliable method for generating new promising compounds focused on the biological context of the disease.

In drug discovery, Deep Learning algorithms are emerging as a potential method to generate novel chemical structures since they can speed up the traditional process and decrease expenditure. Recurrent architectures are amongst the most promising methods for computational de novo drug design. One current challenge consists in finding the optimal architecture and parameters for the recurrent network that assures the generation of valid molecules that span the chemical space. In this work we perform an evaluation on Recurrent Neural Networks which can learn the syntax of molecular representation in terms of SMILES notation. We optimize the computational framework based on the recurrent architecture and its hyper-parameters. Moreover, we evaluate the performance of two types of encoding and spatial arrangement of molecules: Embedding and One-hot Encoding, and datasets with and without stereo-chemical information, respectively. The proposed model showed improved performance when compared to the current literature, both in terms of percentage of valid generated SMILES and diversity with 98.7% and 0.88, for the ChEMBL dataset, respectively. Even when considering the ZINC biogenic library, with stereochemical information, the values were 94.5% and 0.90. The obtained results reveal the potential of the recurrent architectures in learning the SMILES syntax and adding novelty to generate promising compounds.

Machine learning has been increasingly applied with success in generating synthetically reasonable molecules.However, a complete system capable of both producing valid molecules and optimizing multiple traits has remained elusive.This paper employs multiobjective reinforcement learning to draw a framework to design compounds.Different multiobjective techniques have been evaluated, such as weighted sum and Chebyshev.The results show that the implemented model can be effectively optimized towards different and competing molecular properties.Nonetheless, the model implemented with the weighted sum scalarization technique with a weight of 0.55 for biological affinity is the one with the most appropriate trade-off for the different evaluated properties.

This paper provides a generative network framework that can replicate the molecular space distribution to satisfy a set of desirable features.The approach incorporates two effective machine learning techniques: an Encoder-Decoder architecture that converts the string notations of molecules into latent space and a generative adversarial network to learn the data distribution and generate new compounds.We train this joint model on a dataset that includes stereo-chemical information.The results show an improvement in the Encoder-Decoder performance, reaching 89% of correctly reconstructed molecules.The framework can generate a wide variety of compounds biased towards specific molecular properties using Transfer Learning.

MOTIVATION: The process of placing new drugs into the market is time-consuming, expensive and complex. The application of computational methods for designing molecules with bespoke properties can contribute to saving resources throughout this process. However, the fundamental properties to be optimized are often not considered or conflicting with each other. In this work, we propose a novel approach to consider both the biological property and the bioavailability of compounds through a deep reinforcement learning framework for the targeted generation of compounds. We aim to obtain a promising set of selective compounds for the adenosine A2A receptor and, simultaneously, that have the necessary properties in terms of solubility and permeability across the blood-brain barrier to reach the site of action. The cornerstone of the framework is based on a recurrent neural network architecture, the Generator. It seeks to learn the building rules of valid molecules to sample new compounds further. Also, two Predictors are trained to estimate the properties of interest of the new molecules. Finally, the fine-tuning of the Generator was performed with reinforcement learning, integrated with multi-objective optimization and exploratory techniques to ensure that the Generator is adequately biased. RESULTS: The biased Generator can generate an interesting set of molecules, with approximately 85% having the two fundamental properties biased as desired. Thus, this approach has transformed a general molecule generator into a model focused on optimizing specific objectives. Furthermore, the molecules' synthesizability and drug-likeness demonstrate the potential applicability of the de novo drug design in medicinal chemistry. AVAILABILITY AND IMPLEMENTATION: All code is publicly available in the https://github.com/larngroup/De-Novo-Drug-Design. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.