Deep generative model for therapeutic targets using transcriptomic disease-associated data-USP7 case study

Enhancing reinforcement learning for de novo molecular design applying self-attention mechanisms

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).

De Novo Design of Molecules with Multiaction Potential from Differential Gene Expression using Variational Autoencoder

The modulating effect of chemical compounds and therapeutics on gene transcription is well-reported and has been intensively studied for both clinical and research purposes. Emerging research points toward the utility of drug-induced transcriptional alterations in de novo molecular design and highlights the idea of phenotype-matching an expression signature of interest to the structures being designed. In this work, we build an autoencoder-based generative model, BiCEV, around this concept. Our generative autoencoder has demonstrably generated a set of new molecules from gene expression input with notable validity (96%), uniqueness (98%), and internal diversity (0.77). Further, we attempted to validate BiCEV by testing the model on gene-knockdown profiles and combined signatures of synergistic drug pairs. From these investigations, we found the designed structures to be consistently high in collective quality. However, when their similarities to the supposed functional equivalents as determined by shared targets were considered, the findings were somewhat mixed. In spite of this, we believe the generative model merits further development in conjunction with in vitro corroboration to lend itself to being an assistive tool for drug discovery experts, particularly to support the initial stages of hit identification and lead optimization.

Ubiquitin-specific protease 7 (USP7): an emerging drug target for cancer treatment

INTRODUCTION

Ubiquitin-specific protease 7 (USP7) also known as herpesvirus-associated ubiquitin-specific protease (HAUSP) is a well-characterized cysteine protease that belongs to the largest subfamily of deubiquitinating enzymes (DUBs). It is involved in multiple signaling pathways, some of them dysregulated in malignant tumors. USP7 inhibition can lead to cell growth arrest and apoptosis through inhibition of tumor promoters and stabilization of tumor suppressors, making it a promising druggable target for cancer therapy.

AREAS COVERED

This review covers the structure of USP7, its function in multiple signaling pathways and relevance in cancer, as well as recent advances and future perspectives in the development of USP7 inhibitors for cancer therapy.

EXPERT OPINION

Literature reports display the multiple antitumor activities of USP7 inhibitors, both in vitro and in vivo. Nonetheless, none have entered clinical trials so far, highlighting the need to delve into a deeper understanding of USP7 binding sites and the development of more accurate compound screening methods. Despite these challenges, further development of USP7 inhibitors is promising as a valuable new approach for cancer treatment, including the ability to address chemoresistance.

Gex2SGen: Designing Drug-like Molecules from Desired Gene Expression Signatures

Drug-induced gene expression profiling provides a lot of useful information covering various aspects of drug discovery and development. Most importantly, this knowledge can be used to discover drugs' mechanisms of action. Recently, deep learning-based drug design methods are in the spotlight due to their ability to explore huge chemical space and design property-optimized target-specific drug molecules. Recent advances in accessibility of open-source drug-induced transcriptomic data along with the ability of deep learning algorithms to understand hidden patterns have opened opportunities for designing drug molecules based on desired gene expression signatures. In this study, we propose a deep learning model, Gex2SGen (Gene Expression 2 SMILES Generation), to generate novel drug-like molecules based on desired gene expression profiles. The model accepts desired gene expression profiles in a cell-specific manner as input and designs drug-like molecules which can elicit the required transcriptomic profile. The model was first tested against individual gene-knocked-out transcriptomic profiles, where the newly designed molecules showed high similarity with known inhibitors of the knocked-out target genes. The model was next applied on a triple negative breast cancer signature profile, where it could generate novel molecules, highly similar to known anti-breast cancer drugs. Overall, this work provides a generalized method, where the method first learned the molecular signature of a given cell due to a specific condition, and designs new small molecules with drug-like properties.

Highlights in USP7 inhibitors for cancer treatment

Ubiquitin-specific protease 7 (USP7) is a member of one of the most largely studied families of deubiquitylating enzymes. It plays a key role modulating the levels of multiple proteins, including tumor suppressors, transcription factors, epigenetic modulators, DNA repair proteins, and regulators of the immune response. The abnormal expression of USP7 is found in various malignant tumors and a high expression signature generally indicates poor tumor prognosis. This suggests USP7 as a promising prognostic and druggable target for cancer therapy. Nonetheless, no approved drugs targeting USP7 have already entered clinical trials. Therefore, the development of potent and selective USP7 inhibitors still requires intensive research and development efforts before the pre-clinical benefits translate into the clinic. This mini review systematically summarizes the role of USP7 as a drug target for cancer therapeutics, as well as the scaffolds, activities, and binding modes of some of the most representative small molecule USP7 inhibitors reported in the scientific literature. To wind up, development challenges and potential combination therapies using USP7 inhibitors for less tractable tumors are also disclosed.