Machine-Learning-Assisted Approach for Discovering Novel Inhibitors Targeting Bromodomain-Containing Protein 4

Novel small molecule inhibitors targeting renal cell carcinoma: Status, challenges, future directions

Renal cell carcinoma (RCC) is the most common renal malignancy with a rapidly increasing morbidity and mortality rate gradually. RCC has a high mortality rate and an extremely poor prognosis. Despite numerous treatment strategies, RCC is resistant to conventional radiotherapy and chemotherapy. In addition, the limited clinical efficacy and inevitable resistance of multiple agents suggest an unmet clinical need. Therefore, there is an urgent need to develop novel anti-RCC candidates. Nowadays many promising results have been achieved with the development of novel small molecule inhibitors against RCC. This paper reviews the recent research progress of novel small molecule inhibitors targeting RCC. It is focusing on the structural optimization process and conformational relationships of small molecule inhibitors, as well as the potential mechanisms and anticancer activities for the treatment of RCC. To provide a theoretical basis for promoting the clinical translation of novel small molecule inhibitors, we discussed their application prospects and future development directions. It could be capable of improving the clinical efficacy of RCC and improving the therapy resistance for RCC.

Targeting MYC at the intersection between cancer metabolism and oncoimmunology

MYC activation is a known hallmark of cancer as it governs the gene targets involved in various facets of cancer progression. Of interest, MYC governs oncometabolism through the interactions with its partners and cofactors, as well as cancer immunity via its gene targets. Recent investigations have taken interest in characterizing these interactions through multi-Omic approaches, to better understand the vastness of the MYC network. Of the several gene targets of MYC involved in either oncometabolism or oncoimmunology, few of them overlap in function. Prominent interactions have been observed with MYC and HIF-1α, in promoting glucose and glutamine metabolism and activation of antigen presentation on regulatory T cells, and its subsequent metabolic reprogramming. This review explores existing knowledge of the role of MYC in oncometabolism and oncoimmunology. It also unravels how MYC governs transcription and influences cellular metabolism to facilitate the induction of pro- or anti-tumoral immunity. Moreover, considering the significant roles MYC holds in cancer development, the present study discusses effective direct or indirect therapeutic strategies to combat MYC-driven cancer progression.

Discovery of Novel Inhibitors of BRD4 for Treating Prostate Cancer: A Comprehensive Case Study for Considering Water Networks in Virtual Screening and Drug Design

Androgen receptor (AR) is the primary target for treating prostate cancer (PCa), which inevitably progresses due to drug-resistant mutations. Bromodomain-containing protein 4 (BRD4) has been a new potential drug target for PCa treatment. Herein, we report the rational design and discovery of novel BRD4 inhibitors through computer-aided drug design (CADD), and a hit compound SQ-1 (IC50 = 676 nM) was identified by structure-based virtual screening (SBVS) with the conserved water network. To optimize the structure of SQ-1, the free energy landscape was constructed, and the binding mechanism was explored by characterizing the water profile and the dissociation mechanism. Finally, the compound SQ-17 with improved inhibitory activity (IC50 < 100 nM) was discovered, which showed potent antiproliferative activity against LNCaP. These data highlighted a successful attempt to identify and optimize a small molecule by comprehensive CADD application and provided essential clues for developing novel therapeutics for PCa treatment.

Combination of 2-tert-Butyl-1,4-Benzoquinone (TBQ) and ZnO Nanoparticles, a New Strategy To Inhibit Biofilm Formation and Virulence Factors of Chromobacterium violaceum

Drug-resistant bacteria have been raising serious social problems. Bacterial biofilms and different virulence factors are the main reasons for persistent infections. As a conditioned pathogen, Chromobacterium violaceum has evolved a vast network of regulatory mechanisms to modify and fine-tune biofilm development, contributing to multidrug resistance. However, there are few therapies to combat drug-resistant bacteria. Quorum sensing (QS) inhibitors (QSIs) are a promising strategy to solve antibiotic resistance. Our previous work suggested that 2-tert-butyl-1,4-benzoquinone (TBQ) is a potent QSI. In this study, the combination of zinc oxide nanoparticles (ZnO-NPs) and TBQ (ZnO-TBQ) was investigated for the treatment of Chromobacterium violaceum ATCC 12472 infection. ZnO-NPs attach to cell walls or biofilms, and the local dissolution of ZnO-NPs can lead to increased Zn²⁺ concentrations, which could destroy metal homeostasis, corresponding to disturbances in amino acid metabolism and nucleic acid metabolism. ZnO-NPs significantly improved the efficiency of TBQ in inhibiting the QS-related virulence factors and biofilm formation of C. violaceum ATCC 12472. ZnO-TBQ effectively reduces the expression of genes related to QS, which is conducive to limiting the infectivity of C. violaceum ATCC 12472. Caenorhabditis elegans nematodes treated with ZnO-TBQ presented a significant improvement in the survival rate by 46.7%. Overall, the combination of ZnO-NPs and TBQ offers a new strategy to attenuate virulence factors and biofilm formation synergistically in some drug-resistant bacteria. IMPORTANCE The combination of ZnO-NPs and TBQ (ZnO-TBQ) can compete with the inducer N-decanoyl-homoserine lactone (C₁₀-HSL) by binding to CviR and downregulate genes related to the CviI/CviR system to interrupt the QS system of C. violaceum ATCC 12472. The downstream genes responding to cviR were also downregulated so that virulence factors and biofilm formation were inhibited. Furthermore, ZnO-TBQ presents multiple metabolic disturbances in C. violaceum ATCC 12472, which results in the reduced multidrug resistance and pathogenicity of C. violaceum ATCC 12472. In an in vivo assay, C. elegans nematodes treated with ZnO-TBQ presented a significant improvement in the survival rate by 46.7% by limiting the infectivity of C. violaceum ATCC 12472. In addition, ZnO-TBQ inhibited the generation of virulence factors and biofilm formation 2-fold compared to either ZnO-NPs or TBQ alone. The combination of ZnO-NPs with TBQ offers a potent synergistic strategy to reduce multidrug resistance and pathogenicity.

Artificial Intelligence and Machine Learning Technology Driven Modern Drug Discovery and Development

The discovery and advances of medicines may be considered as the ultimate relevant translational science effort that adds to human invulnerability and happiness. But advancing a fresh medication is a quite convoluted, costly, and protracted operation, normally costing USD ~2.6 billion and consuming a mean time span of 12 years. Methods to cut back expenditure and hasten new drug discovery have prompted an arduous and compelling brainstorming exercise in the pharmaceutical industry. The engagement of Artificial Intelligence (AI), including the deep-learning (DL) component in particular, has been facilitated by the employment of classified big data, in concert with strikingly reinforced computing prowess and cloud storage, across all fields. AI has energized computer-facilitated drug discovery. An unrestricted espousing of machine learning (ML), especially DL, in many scientific specialties, and the technological refinements in computing hardware and software, in concert with various aspects of the problem, sustain this progress. ML algorithms have been extensively engaged for computer-facilitated drug discovery. DL methods, such as artificial neural networks (ANNs) comprising multiple buried processing layers, have of late seen a resurgence due to their capability to power automatic attribute elicitations from the input data, coupled with their ability to obtain nonlinear input-output pertinencies. Such features of DL methods augment classical ML techniques which bank on human-contrived molecular descriptors. A major part of the early reluctance concerning utility of AI in pharmaceutical discovery has begun to melt, thereby advancing medicinal chemistry. AI, along with modern experimental technical knowledge, is anticipated to invigorate the quest for new and improved pharmaceuticals in an expeditious, economical, and increasingly compelling manner. DL-facilitated methods have just initiated kickstarting for some integral issues in drug discovery. Many technological advances, such as "message-passing paradigms", "spatial-symmetry-preserving networks", "hybrid de novo design", and other ingenious ML exemplars, will definitely come to be pervasively widespread and help dissect many of the biggest, and most intriguing inquiries. Open data allocation and model augmentation will exert a decisive hold during the progress of drug discovery employing AI. This review will address the impending utilizations of AI to refine and bolster the drug discovery operation.

Mechanistic Analysis of Chemically Diverse Bromodomain-4 Inhibitors Using Balanced QSAR Analysis and Supported by X-ray Resolved Crystal Structures

Bromodomain-4 (BRD-4) is a key enzyme in post-translational modifications, transcriptional activation, and many other cellular processes. Its inhibitors find their therapeutic usage in cancer, acute heart failure, and inflammation to name a few. In the present study, a dataset of 980 molecules with a significant diversity of structural scaffolds and composition was selected to develop a balanced QSAR model possessing high predictive capability and mechanistic interpretation. The model was built as per the OECD (Organisation for Economic Co-operation and Development) guidelines and fulfills the endorsed threshold values for different validation parameters (R²_tr = 0.76, Q²_LMO = 0.76, and R²_ex = 0.76). The present QSAR analysis identified that anti-BRD-4 activity is associated with structural characters such as the presence of saturated carbocyclic rings, the occurrence of carbon atoms near the center of mass of a molecule, and a specific combination of planer or aromatic nitrogen with ring carbon, donor, and acceptor atoms. The outcomes of the present analysis are also supported by X-ray-resolved crystal structures of compounds with BRD-4. Thus, the QSAR model effectively captured salient as well as unreported hidden pharmacophoric features. Therefore, the present study successfully identified valuable novel pharmacophoric features, which could be beneficial for the future optimization of lead/hit compounds for anti-BRD-4 activity.

Artificial intelligence in virtual screening: models versus experiments

A typical drug discovery project involves identifying active compounds with significant binding potential for selected disease-specific targets. Experimental high-throughput screening (HTS) is a traditional approach to drug discovery, but is expensive and time-consuming when dealing with huge chemical libraries with billions of compounds. The search space can be narrowed down with the use of reliable computational screening approaches. In this review, we focus on various machine-learning (ML) and deep-learning (DL)-based scoring functions developed for solving classification and ranking problems in drug discovery. We highlight studies in which ML and DL models were successfully deployed to identify lead compounds for which the experimental validations are available from bioassay studies.

Recent Advances in Application of Computer-Aided Drug Design in Anti-Influenza A Virus Drug Discovery

Influenza A is an acute respiratory infectious disease caused by the influenza A virus, which seriously threatens global human health and causes substantial economic losses every year. With the emergence of new viral strains, anti-influenza drugs remain the most effective treatment for influenza A. Research on traditional, innovative small-molecule drugs faces many challenges, while computer-aided drug design (CADD) offers opportunities for the rapid and effective development of innovative drugs. This literature review describes the general process of CADD, the viral proteins that play an essential role in the life cycle of the influenza A virus and can be used as therapeutic targets for anti-influenza drugs, and examples of drug screening of viral target proteins by applying the CADD approach. Finally, the main limitations of current CADD strategies in anti-influenza drug discovery and the field's future directions are discussed.

Theoretical exploration of the binding selectivity of inhibitors to BRD7 and BRD9 with multiple short molecular dynamics simulations

Bromodomain-containing proteins 7 and 9 (BRD7 and BRD9) have been considered as potential targets of clinical drug design toward treatment of human cancers and other diseases. Multiple short molecular dynamics simulations and binding free energy predictions were carried out to decipher the binding selectivity of three inhibitors 4L2, 5U6, and 6KT toward BRD7 and BRD9. The results show that 4L2 has more favorable binding ability to BRD7 over BRD9 compared to 5U6 and 6KT, while 5U6 and 6KT possess more favorable associations with BRD9 than BRD7. Furthermore, estimations of residue-based free energy decompositions further identify that four common residue pairs, including (F155, F44), (V160, V49), (Y168, Y57) and (Y217, Y106) in (BRD7, BRD9) generate obvious binding differences with 4L2, 5U6, and 6KT, which mostly drives the binding selectivity of 4L2, 5U6, and 6KT to BRD7 and BRD9. Dynamic information arising from trajectory analysis also suggests that inhibitor bindings affect structural flexibility and motion modes, which is responsible for the partial selectivity of 4L2, 5U6, and 6KT toward BRD7 and BRD9. As per our expectation, this study theoretically provides useful hints for design of dual inhibitors with high selectivity on BRD7 and BRD9.

Bromodomains (BRDs) are structurally conserved epigenetic reader modules observed in numerous chromatin- and transcription-associated proteins that have a capability to identify acetylated lysine residues.

Recent contributions of quinolines to antimalarial and anticancer drug discovery research

Quinoline, a privileged scaffold in medicinal chemistry, has always been associated with a multitude of biological activities. Especially in antimalarial and anticancer research, quinoline played (and still plays) a central role, giving rise to the development of an array of quinoline-containing pharmaceuticals in these therapeutic areas. However, both diseases still affect millions of people every year, pointing to the necessity of new therapies. Quinolines have a long-standing history as antimalarial agents, but established quinoline-containing antimalarial drugs are now facing widespread resistance of the Plasmodium parasite. Nevertheless, as evidenced by a massive number of recent literature contributions, they are still of great value for future developments in this field. On the other hand, the number of currently approved anticancer drugs containing a quinoline scaffold are limited, but a strong increase and interest in quinoline compounds as potential anticancer agents can be seen in the last few years. In this review, a literature overview of recent contributions made by quinoline-containing compounds as potent antimalarial or anticancer agents is provided, covering publications between 2018 and 2020.

Machine Learning in Drug Discovery: A Review

This review provides the feasible literature on drug discovery through ML tools and techniques that are enforced in every phase of drug development to accelerate the research process and deduce the risk and expenditure in clinical trials. Machine learning techniques improve the decision-making in pharmaceutical data across various applications like QSAR analysis, hit discoveries, de novo drug architectures to retrieve accurate outcomes. Target validation, prognostic biomarkers, digital pathology are considered under problem statements in this review. ML challenges must be applicable for the main cause of inadequacy in interpretability outcomes that may restrict the applications in drug discovery. In clinical trials, absolute and methodological data must be generated to tackle many puzzles in validating ML techniques, improving decision-making, promoting awareness in ML approaches, and deducing risk failures in drug discovery.

In Silico Prediction of CYP2C8 Inhibition with Machine-Learning Methods

Cytochrome P450 2C8 (CYP2C8) is a major drug-metabolizing enzyme in humans and is responsible for the metabolism of ∼5% drugs in clinical use. Thus, inhibition of CYP2C8, which causes potential adverse drug events, cannot be neglected. The in vitro drug interaction studies guidelines for industry issued by the FDA also point out that it needs to be determined whether investigated drugs are CYP2C8 inhibitors before clinical trials. However, current studies mainly focus on predicting the inhibitors of other major P450 enzymes, and the importance of CYP2C8 inhibition has been overlooked. Therefore, there is a need to develop models for identifying potential CYP2C8 inhibition. In this study, in silico classification models for predicting CYP2C8 inhibition were built by five machine-learning methods combined with nine molecular fingerprints. The performance of the models built was evaluated by test and external validation sets. The best model had AUC values of 0.85 and 0.90 for the test and external validation sets, respectively. The applicability domain was analyzed based on the molecular similarity and exhibited an impact on the improvement of prediction accuracy. Furthermore, several representative privileged substructures such as 1H-benzo[d]imidazole, 1-phenyl-1H-pyrazole, and quinoline were identified by information gain and substructure frequency analysis. Overall, our results would be helpful for the prediction of CYP2C8 inhibition.

Computational representations of protein-ligand interfaces for structure-based virtual screening

Introduction: Structure-based virtual screening (SBVS) is an essential strategy for hit identification. SBVS primarily uses molecular docking, which exploits the protein-ligand binding mode and associated affinity score for compound ranking. Previous studies have shown that computational representation of protein-ligand interfaces and the later establishment of machine learning models are efficacious in improving the accuracy of SBVS.Areas covered: The authors review the computational methods for representing protein-ligand interfaces, which include the traditional ones that use deliberately designed fingerprints and descriptors and the more recent methods that automatically extract features with deep learning. The effects of these methods on the performance of machine learning models are briefly discussed. Additionally, case studies that applied various computational representations to machine learning are cited with remarks.Expert opinion: It has become a trend to extract binding features automatically by deep learning, which uses a completely end-to-end representation. However, there is still plenty of scope for improvement . The interpretability of deep-learning models, the organization of data management, the quantity and quality of available data, and the optimization of hyperparameters could impact the accuracy of feature extraction. In addition, other important structural factors such as water molecules and protein flexibility should be considered.

Cytoplasmic DNA sensing by KU complex in aged CD4+ T cell potentiates T cell activation and aging-related autoimmune inflammation

Aging is associated with DNA accumulation and increased homeostatic proliferation of circulating T cells. Although these attributes are associated with aging-related autoimmunity, their direct contributions remain unclear. Conventionally, KU complex, the regulatory subunit of DNA-dependent protein kinase (DNA-PK), together with the catalytic subunit of DNA-PK (DNA-PKcs), mediates DNA damage repair in the nucleus. Here, we found KU complex abundantly expressed in the cytoplasm, where it recognized accumulated cytoplasmic DNA in aged human and mouse CD4⁺ T cells. This process enhanced T cell activation and pathology of experimental autoimmune encephalomyelitis (EAE) in aged mice. Mechanistically, KU-mediated DNA sensing facilitated DNA-PKcs recruitment and phosphorylation of the kinase ZAK. This activated AKT and mTOR pathways, promoting CD4⁺ T cell proliferation and activation. We developed a specific ZAK inhibitor, which dampened EAE pathology in aged mice. Overall, these findings demonstrate a KU-mediated cytoplasmic DNA-sensing pathway in CD4⁺ T cells that potentiates aging-related autoimmunity.

Machine learning approaches for elucidating the biological effects of natural products

Covering: 2000 to 2020 Machine learning (ML) is an efficient tool for the prediction of bioactivity and the study of structure-activity relationships. Over the past decade, an emerging trend for combining these approaches with the study of natural products (NPs) has developed in order to manage the challenge of the discovery of bioactive NPs. In the present review, we will introduce the basic principles and protocols for using the ML approach to investigate the bioactivity of NPs, citing a series of practical examples regarding the study of anti-microbial, anti-cancer, and anti-inflammatory NPs, etc. ML algorithms manage a variety of classification and regression problems associated with bioactive NPs, from those that are linear to non-linear and from pure compounds to plant extracts. Inspired by cases reported in the literature and our own experience, a number of key points have been emphasized for reducing modeling errors, including dataset preparation and applicability domain analysis.

Design, synthesis, and biological evaluation of quinazolin-4(3H)-one derivatives co-targeting poly(ADP-ribose) polymerase-1 and bromodomain containing protein 4 for breast cancer therapy

This study was aimed to design the first dual-target small-molecule inhibitor co-targeting poly (ADP-ribose) polymerase-1 (PARP1) and bromodomain containing protein 4 (BRD4), which had important cross relation in the global network of breast cancer, reflecting the synthetic lethal effect. A series of new BRD4 and PARP1 dual-target inhibitors were discovered and synthesized by fragment-based combinatorial screening and activity assays that together led to the chemical optimization. Among these compounds, 19d was selected and exhibited micromole enzymatic potencies against BRD4 and PARP1, respectively. Compound 19d was further shown to efficiently modulate the expression of BRD4 and PARP1. Subsequently, compound 19d was found to induce breast cancer cell apoptosis and stimulate cell cycle arrest at G1 phase. Following pharmacokinetic studies, compound 19d showed its antitumor activity in breast cancer susceptibility gene 1/2 (BRCA1/2) wild-type MDA-MB-468 and MCF-7 xenograft models without apparent toxicity and loss of body weight. These results together demonstrated that a highly potent dual-targeted inhibitor was successfully synthesized and indicated that co-targeting of BRD4 and PARP1 based on the concept of synthetic lethality would be a promising therapeutic strategy for breast cancer.

Current advances on the development of BET inhibitors: insights from computational methods

Epigenetics was coined almost 70 years ago for the description of heritable phenotype without altering DNA sequences. Research on the field has uncovered significant roles of such mechanisms, that account for the biogenesis of several diseases. Further studies have led the way for drug development which targets epi-enzymes, mainly for cancer treatment. Of the numerous epi-targets involved with histone acetylation, bromodomains have captured the spotlight of drug discovery focused on novel therapies. However, due to high sequence identity, the development of potent and selective inhibitors poses a significant challenge. Herein, we discuss recent computational developments on BET inhibitors and other methods that may be applied for drug discovery in general. As a proof-of-concept, we discuss a virtual screening to identify novel BET inhibitors based on coumarin derivatives. From public data, we identified putative structure-activity relationships of coumarin scaffold and propose R-group modifications for BET selectivity. Results showed that the optimization and design of novel coumarins could be further explored.

Revealing binding selectivity of inhibitors toward bromodomain-containing proteins 2 and 4 using multiple short molecular dynamics simulations and free energy analyses

Emerging evidences indicate bromodomain-containing proteins 2 and 4 (BRD2 and BRD4) play critical roles in cancers, inflammations, cardiovascular diseases and other pathologies. Multiple short molecular dynamics (MSMD) simulations combined with molecular mechanics generalized Born surface area (MM-GBSA) method were applied to investigate the binding selectivity of three inhibitors 87D, 88M and 89G towards BRD2 over BRD4. The root-mean-square fluctuation (RMSF) analysis indicates that the structural flexibility of BRD4 is stronger than that of BRD2. Moreover the calculated distances between the C_α atoms in the centres of the ZA_loop and BC_loop of BRD4 are also bigger than that of BRD2. The rank of binding free energies calculated using MM-GBSA method agrees well with that determined by experimental data. The results show that 87D can bind more favourably to BRD2 than BRD4, while 88M has better selectivity on BRD4 over BRD2. Residue-based free-energy decomposition method was utilized to estimate the inhibitor-residue interaction spectrum and the results not only identify the hot interaction spots of inhibitors with BRD2 and BRD4, but also demonstrate that several common residues, including (W370, W374), (P371, P375), (V376, V380) and (L381, L385) belonging to (BRD2, BRD4), generate significant binding difference of inhibitors to BRD2 and BRD4.

Discovery of novel glyceraldehyde-3-phosphate dehydrogenase inhibitor via docking-based virtual screening

Glycolysis is enhanced in cancer cells. Cancer cells utilize glycolysis as their primary energy source, even under aerobic conditions. This is known as the Warburg effect. Thus, effective inhibition of the glycolytic pathway is a crucial component of cancer therapy. Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) is an important enzyme in glycolysis and overexpresses in cancers. Therefore, targeting GAPDH to inhibit its role in glycolysis is important for GAPDH functional studies and the treatment of cancers. However, only a few GAPDH inhibitors have been reported. In our current study, we identified a GAPDH inhibitor, DC-5163, using docking-based virtual screening and biochemical and biophysical analysis. DC-5163 is a small molecule compound that inhibits GAPDH enzyme activity and cancer cell proliferation (normal cells were tolerant to it). It can inhibit glycolysis pathway partially, which was manifested by decreased glucose uptake and lactic acid production. And it also leaded to cell death through apoptotic pathways. This study reflects the pivotal role of GAPDH in cancer cells and demonstrates that DC-5163 is a useful inhibitor and can be of value in studying the role of GAPDH and the development of new clinical cancer treatments.

A concise review of recent advances in anti-heart failure targets and its small molecules inhibitors in recent years

Heart failure is a disease with high mortality and the risk of heart failure increases in magnitude with age. The patients of heart failure is increasing year by year. Hence, Pharmaceutical researchers have to develop new drugs with better pharmacological effects to coping with this phenomenon. In this article, we reviewed the small molecule compounds for heart failure that have been marketed in recent years or are promising to enter clinical research. We also reviewed the SAR (structure activity relationship) of these molecules, such as renin inhibitors, ROMK inhibitors, a kind of new diuretics, and some dual-targets inhibitors. These small molecules proven to be beneficial effect on heart failure patients. Which may provide ideas for the design of novel anti-heart failure therapeutic drugs.

Discovery of betulinaldehyde as a natural RORγt agonist

Retinoic Acid Receptor-Related Orphan Receptor γt (RORγt) is a dual-functional therapeutic target. The agonists and inhibitors of RORγt are potential agents for tumor immunotherapy and autoimmune diseases, respectively, and sometimes share similar scaffolds. Although the widely distributed triterpenoid ursolic acid (UA) has been identified as a RORγt inhibitor, the report of a triterpenoid RORγt agonist is still absent. By screening an in-house triterpenoid library, we uncovered a novel RORγt agonist, betulinaldehyde (1), together with an inhibitor (2, 3β, 28-Dihydroxy-lupan-29-oic acid). Compound 1 showed a good RORγt activating effect with the EC₅₀ of 11.4 μM in Alpha Screen assay, and altered the thermal stability of RORγt by directly binding to the protein in vitro. Combined with the SPR assay, the Kd value of compound 1 was examined as 2.99 μM. The modulation mechanism of triterpenoid agonists and inhibitors were discussed by molecular docking. Herein, we firstly discovered compound 1 as a triterpenoid agonist of RORγt. The co-distribution of triterpenoid RORγt agonist and inhibitors in the same plant, might be related to the anti-inflammatory and anti-cancerous bioactivity of the plant extract.

Deep Neural Network Classifier for Virtual Screening Inhibitors of (S)-Adenosyl-L-Methionine (SAM)-Dependent Methyltransferase Family

The (S)-adenosyl-L-methionine (SAM)-dependent methyltransferases play essential roles in post-translational modifications (PTMs) and other miscellaneous biological processes, and are implicated in the pathogenesis of various genetic disorders and cancers. Increasing efforts have been committed toward discovering novel PTM inhibitors targeting the (S)-Adenosyl-L-methionine (SAM)-binding site and the substrate-binding site of methyltransferases, among which virtual screening (VS) and structure-based drug design (SBDD) are the most frequently used strategies. Here, we report the development of a target-specific scoring model for compound VS, which predict the likelihood of the compound being a potential inhibitor for the SAM-binding pocket of a given methyltransferase. Protein-ligand interaction characterized by Fingerprinting Triplets of Interaction Pseudoatoms was used as the input feature, and a binary classifier based on deep neural networks is trained to build the scoring model. This model enhances the efficiency of the existing strategies used for discovering novel chemical modulators of methyltransferase, which is crucial for understanding and exploring the complexity of epigenetic target space.

Discovery of trisubstituted nicotinonitrile derivatives as novel human GCN5 inhibitors through AlphaScreen-based high throughput screening

The general control nonrepressed protein 5 (GCN5) is an important target for drug design and drug discovery largely owing to its pathogenic role in malignancies. Chemical probes that target GCN5 have been developed in recent decades, but their potencies are still unsatisfactory. In this study, through an in-house developed AlphaScreen-based high throughput screening platform, radioactive acetylation assays and 2D-similarity based analogue searching, we discovered DC_HG24-01 as the novel hGCN5 inhibitor with the IC50 value of 3.1 ± 0.2 μM. Further docking studies suggested that DC_HG24-01 could occupy the binding pocket of acetyl-CoA cofactor, which laid the foundation for the development of more potent hGCN5 inhibitors in the future. At the cellular level, DC_HG24-01 could retard cell proliferation and block the acetylation of H3K14 leading to cell apoptosis and cell cycle arrest at the G1 phase in MV4-11 cell lines. Taken together, the discovery of DC_HG24-01 may serve as a good starting point to accelerate the development of more potent hGCN5 inhibitors through further structural decoration and provide new insight into the pharmacological treatment of leukemia.

Discovery and biological evaluation of thiobarbituric derivatives as potent p300/CBP inhibitors

Histone acetyltransferases (HATs) relieve transcriptional repression by preferentially acetylation of ε-amino group of lysine residues on histones. Dysregulation of HATs is strongly correlated with etiology of several diseases especially cancer, thus highlighting the utmost significance of the development of small molecule inhibitors against this potential therapeutic target. In the present study, through virtual screening and iterative optimization, we identified DCH36_06 as a bona fide, potent p300/CBP inhibitor. DCH36_06 mediated p300/CBP inhibition leading to hypoacetylation on H3K18 in leukemic cells. The suppression of p300/CBP activity retarded cell proliferation in several leukemic cell lines. In addition, DCH36_06 arrested cell cycle at G1 phase and induced apoptosis via activation of capase3, caspase9 and PARP that elucidated the molecular mechanism of its anti-proliferation activity. In transcriptome analysis, DCH36_06 altered downstream gene expression and apoptotic pathways-related genes verified by real-time PCR. Importantly, DCH36_06 blocked the leukemic xenograft growth in mice supporting its potential for in vivo use that underlies the therapeutic potential for p300/CBP inhibitors in clinical translation. Taken together, our findings suggest that DCH36_06 may serve as a qualified chemical tool to decode the acetylome code and open up new opportunities for clinical intervention.

Artificial intelligence in drug design

Thanks to the fast improvement of the computing power and the rapid development of the computational chemistry and biology, the computer-aided drug design techniques have been successfully applied in almost every stage of the drug discovery and development pipeline to speed up the process of research and reduce the cost and risk related to preclinical and clinical trials. Owing to the development of machine learning theory and the accumulation of pharmacological data, the artificial intelligence (AI) technology, as a powerful data mining tool, has cut a figure in various fields of the drug design, such as virtual screening, activity scoring, quantitative structure-activity relationship (QSAR) analysis, de novo drug design, and in silico evaluation of absorption, distribution, metabolism, excretion and toxicity (ADME/T) properties. Although it is still challenging to provide a physical explanation of the AI-based models, it indeed has been acting as a great power to help manipulating the drug discovery through the versatile frameworks. Recently, due to the strong generalization ability and powerful feature extraction capability, deep learning methods have been employed in predicting the molecular properties as well as generating the desired molecules, which will further promote the application of AI technologies in the field of drug design.

Computer-Aided Drug Design in Epigenetics

Epigenetic dysfunction has been widely implicated in several diseases especially cancers thus highlights the therapeutic potential for chemical interventions in this field. With rapid development of computational methodologies and high-performance computational resources, computer-aided drug design has emerged as a promising strategy to speed up epigenetic drug discovery. Herein, we make a brief overview of major computational methods reported in the literature including druggability prediction, virtual screening, homology modeling, scaffold hopping, pharmacophore modeling, molecular dynamics simulations, quantum chemistry calculation, and 3D quantitative structure activity relationship that have been successfully applied in the design and discovery of epi-drugs and epi-probes. Finally, we discuss about major limitations of current virtual drug design strategies in epigenetics drug discovery and future directions in this field.

Discovery of 1,8-acridinedione derivatives as novel GCN5 inhibitors via high throughput screening

The general control nonrepressed protein 5 (GCN5) plays a crucial role in many biological processes. Dysregulation of GCN5 has been closely related to various human diseases, especially cancers. Hence, the exploitation of small molecules targeting GCN5 is essential for drug design and academic research. Based on the amplified luminescent proximity homogeneous assay screen methodology, we performed high throughput screening and discovered a novel GCN5 inhibitor DC_G16 with 1,8-acridinedione scaffold. Structure optimization led to the identification of a highly potent inhibitor, namely DC_G16-11 with the half-maximal inhibitory concentration (IC₅₀) value of 6.8 μM. The binding between DC_G16-11 and GCN5 was demonstrated by NMR and SPR with a K_D of 4.2 μM. It could also inhibit proliferation and induce cell cycle arrest and apoptosis in cancer cells while it presented minimal effects on normal cells. Herein, DC_G16-11 could be applied as a validated chemical probe for GCN5-related biological function research and presented great potential for clinical disease treatment.

Discovery of novel BET inhibitors by drug repurposing of nitroxoline and its analogues

The BET family of bromodomain-containing proteins (BRDs) is believed to be a promising drug target for therapeutic intervention in a number of diseases.