Recent progress on cheminformatics approaches to epigenetic drug discovery.
TL;DR: The advances in computational approaches to drug discovery of small molecules with epigenetic modulation profiles are reviewed, the current chemogenomics data available for epigenetics targets are summarized, and a perspective on the greater utility of biomedical knowledge mining as a means to advance the epigenetic drug discovery is provided.
About: This article is published in Drug Discovery Today.The article was published on 2020-09-30. It has received 22 citations till now. The article focuses on the topics: Chemogenomics & Cheminformatics.
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TL;DR: The Deep Docking (DD) platform as discussed by the authors enables up to 100-fold acceleration of structure-based virtual screening by docking only a subset of a chemical library, iteratively synchronized with a ligand-based prediction of the remaining docking scores.
Abstract: With the recent explosion of chemical libraries beyond a billion molecules, more efficient virtual screening approaches are needed. The Deep Docking (DD) platform enables up to 100-fold acceleration of structure-based virtual screening by docking only a subset of a chemical library, iteratively synchronized with a ligand-based prediction of the remaining docking scores. This method results in hundreds- to thousands-fold virtual hit enrichment (without significant loss of potential drug candidates) and hence enables the screening of billion molecule-sized chemical libraries without using extraordinary computational resources. Herein, we present and discuss the generalized DD protocol that has been proven successful in various computer-aided drug discovery (CADD) campaigns and can be applied in conjunction with any conventional docking program. The protocol encompasses eight consecutive stages: molecular library preparation, receptor preparation, random sampling of a library, ligand preparation, molecular docking, model training, model inference and the residual docking. The standard DD workflow enables iterative application of stages 3-7 with continuous augmentation of the training set, and the number of such iterations can be adjusted by the user. A predefined recall value allows for control of the percentage of top-scoring molecules that are retained by DD and can be adjusted to control the library size reduction. The procedure takes 1-2 weeks (depending on the available resources) and can be completely automated on computing clusters managed by job schedulers. This open-source protocol, at https://github.com/jamesgleave/DD_protocol , can be readily deployed by CADD researchers and can significantly accelerate the effective exploration of ultra-large portions of a chemical space.
50 citations
TL;DR: In this article, a large-scale study of 26 318 compounds with a quantitative measure of biological activity for 55 protein targets with epigenetic activity was conducted. And the results indicated that the models reported herein have considerable potential to identify small molecules with epigenetics activity.
Abstract: Epigenetic targets are of significant importance in drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represents many structure-activity relationships that have not been exploited thus far to develop predictive models to support medicinal chemistry efforts. Herein, we report the first large-scale study of 26 318 compounds with a quantitative measure of biological activity for 55 protein targets with epigenetic activity. We built predictive models with high accuracy for small molecules' epigenetic target profiling through a systematic comparison of the machine learning models trained on different molecular fingerprints. The models were thoroughly validated, showing mean precisions of up to 0.952 for the epigenetic target prediction task. Our results indicate that the models reported herein have considerable potential to identify small molecules with epigenetic activity. Therefore, our results were implemented as a freely accessible web application.
16 citations
TL;DR: The Epigenetic Target Profiler (ETP) as discussed by the authors uses a consensus model based on two binary classification models for each target, relying on support vector machines and built on molecular fingerprints of different design.
Abstract: The identification of protein targets of small molecules is essential for drug discovery. With the increasing amount of chemogenomic data in the public domain, multiple ligand-based models for target prediction have emerged. However, these models are generally biased by the number of known ligands for different targets, which involves an under-representation of epigenetic targets, and despite the increasing importance of epigenetic targets in drug discovery, there are no open tools for epigenetic target prediction. In this work, we introduce Epigenetic Target Profiler (ETP), a freely accessible and easy-to-use web application for the prediction of epigenetic targets of small molecules. For a query compound, ETP predicts its bioactivity profile over a panel of 55 different epigenetic targets. To that aim, ETP uses a consensus model based on two binary classification models for each target, relying on support vector machines and built on molecular fingerprints of different design. A distance-to-model parameter related to the reliability of the predictions is included to facilitate their interpretability and assist in the identification of small molecules with potential epigenetic activity. Epigenetic Target Profiler is freely available at http://www.epigenetictargetprofiler.com.
12 citations
TL;DR: The KDM4 gene family includes more than 30 members, grouped into six subfamilies and two classes based on their sequency homology and catalytic mechanisms, respectively as mentioned in this paper.
Abstract: Breast cancer (BC) is the second leading cause of cancer death in women, although recent scientific and technological achievements have led to significant improvements in progression-free disease and overall survival of patients. Genetic mutations and epigenetic modifications play a critical role in deregulating gene expression, leading to uncontrolled cell proliferation and cancer progression. Aberrant histone modifications are one of the most frequent epigenetic mechanisms occurring in cancer. In particular, methylation and demethylation of specific lysine residues alter gene accessibility via histone lysine methyltransferases (KMTs) and histone lysine demethylases (KDMs). The KDM family includes more than 30 members, grouped into six subfamilies and two classes based on their sequency homology and catalytic mechanisms, respectively. Specifically, the KDM4 gene family comprises six members, KDM4A-F, which are associated with oncogene activation, tumor suppressor silencing, alteration of hormone receptor downstream signaling, and chromosomal instability. Blocking the activity of KDM4 enzymes renders them "druggable" targets with therapeutic effects. Several KDM4 inhibitors have already been identified as anticancer drugs in vitro in BC cells. However, no KDM4 inhibitors have as yet entered clinical trials due to a number of issues, including structural similarities between KDM4 members and conservation of the active domain, which makes the discovery of selective inhibitors challenging. Here, we summarize our current knowledge of the molecular functions of KDM4 members in BC, describe currently available KDM4 inhibitors, and discuss their potential use in BC therapy.
11 citations
TL;DR: In this article, a protein-knockdown strategy was used to identify Lysine demethylase 5 C (KDM5C) degraders, which inhibited the growth of prostate cancer PC-3 cells more strongly than compound 1.
Abstract: Lysine demethylase 5 C (KDM5C) controls epigenetic gene expression and is attracting great interest in the field of chemical epigenetics. KDM5C has emerged as a therapeutic target for anti-prostate cancer agents, and recently we identified triazole 1 as an inhibitor of KDM5C. Compound 1 exhibited highly potent KDM5C-inhibitory activity in in vitro enzyme assays, but did not show strong anticancer effects. Therefore, a different approach is needed for the development of anticancer agents targeting KDM5C. Here, we attempted to identify KDM5C degraders by focusing on a protein-knockdown strategy. Compound 3 b, which was designed based on compound 1, degraded KDM5C and inhibited the growth of prostate cancer PC-3 cells more strongly than compound 1. These findings suggest that KDM5C degraders are more effective as anticancer agents than compounds that only inhibit the catalytic activity of KDM5C.
10 citations
References
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TL;DR: This account of epigenetics in cancer reviews the mechanisms and consequences of epigenetic changes in cancer cells and concludes with the implications of these changes for the diagnosis, prognosis, and treatment of cancer.
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TL;DR: The ReLeaSE method is used to design chemical libraries with a bias toward structural complexity or toward compounds with maximal, minimal, or specific range of physical properties, such as melting point or hydrophobicity.
Abstract: We have devised and implemented a novel computational strategy for de novo design of molecules with desired properties termed ReLeaSE (Reinforcement Learning for Structural Evolution). On the basis of deep and reinforcement learning (RL) approaches, ReLeaSE integrates two deep neural networks—generative and predictive—that are trained separately but are used jointly to generate novel targeted chemical libraries. ReLeaSE uses simple representation of molecules by their simplified molecular-input line-entry system (SMILES) strings only. Generative models are trained with a stack-augmented memory network to produce chemically feasible SMILES strings, and predictive models are derived to forecast the desired properties of the de novo–generated compounds. In the first phase of the method, generative and predictive models are trained separately with a supervised learning algorithm. In the second phase, both models are trained jointly with the RL approach to bias the generation of new chemical structures toward those with the desired physical and/or biological properties. In the proof-of-concept study, we have used the ReLeaSE method to design chemical libraries with a bias toward structural complexity or toward compounds with maximal, minimal, or specific range of physical properties, such as melting point or hydrophobicity, or toward compounds with inhibitory activity against Janus protein kinase 2. The approach proposed herein can find a general use for generating targeted chemical libraries of novel compounds optimized for either a single desired property or multiple properties.
792 citations
TL;DR: Some of the general characteristics of an epigenotype are described, with special reference to the fruit-fly Drosophila melanogaster, as well as a whole complex of development processes, for which the name 'epigenotype' is proposed.
Abstract: The adult characteristics of animals, i.e. their phenotypes, must be studied in order to reach conclusions about the genotypes, i.e. the hereditary constitutions which form the basic subject-matter of genetics. But between genotype and phenotype lies a whole complex of development processes, for which Dr Waddington proposes the name 'epigenotype.' He here describes some of the general characteristics of an epigenotype, with special reference to the fruit-fly Drosophila melanogaster.
691 citations
TL;DR: A machine learning model allows the identification of new small-molecule kinase inhibitors in days and is used to discover potent inhibitors of discoidin domain receptor 1 (DDR1), a kinase target implicated in fibrosis and other diseases, in 21 days.
Abstract: We have developed a deep generative model, generative tensorial reinforcement learning (GENTRL), for de novo small-molecule design. GENTRL optimizes synthetic feasibility, novelty, and biological activity. We used GENTRL to discover potent inhibitors of discoidin domain receptor 1 (DDR1), a kinase target implicated in fibrosis and other diseases, in 21 days. Four compounds were active in biochemical assays, and two were validated in cell-based assays. One lead candidate was tested and demonstrated favorable pharmacokinetics in mice.
663 citations
TL;DR: The authors review recent advances and current debates in epigenetics, including how epigenetic mechanisms interact with genetic variation, ageing, disease and the environment.
Abstract: Epigenetic research has accelerated rapidly in the twenty-first century, generating justified excitement and hope, but also a degree of hype. Here we review how the field has evolved over the last few decades and reflect on some of the recent advances that are changing our understanding of biology. We discuss the interplay between epigenetics and DNA sequence variation as well as the implications of epigenetics for cellular memory and plasticity. We consider the effects of the environment and both intergenerational and transgenerational epigenetic inheritance on biology, disease and evolution. Finally, we present some new frontiers in epigenetics with implications for human health.
640 citations