Chemoinformatics using dimensionality reduction and clustering for enzyme commission number prediction in organic synthesis

The outbreak of COVID-19 has increased the demand for new drug development. That has led to a growing interest in chemoinformatics, which is valuable information technology to predict chemical reactions. The use of enzymes as catalysts is gaining importance in terms of the environment and reaction efficiency. In order to predict the best enzyme to obtain the desired product, the target chemical equation is compared with typical chemical equations of enzymes classified by Enzyme Commission number (EC number) using clustering. The EC number of the chemical equation that is evaluated to have the highest similarity is predicted. © 2023, ICIC International. All rights reserved.

Chemoinformatics approach to design and develop vanillin analogs as COX-1 inhibitor

Background: Coronary Heart Disease (CHD), commonly known as the silent killer, impacted the severity of COVID-19 patients during the pandemic era. Thrombosis or blood clots create the buildup of plaque on the coronary artery walls of the heart, which leads to coronary heart disease. Cyclooxygenase 1 (COX-1) is involved in the production of prostacyclin by systemic arteries;hence, inhibiting the COX-1 enzyme can prevent platelet reactivity mediated by prostacyclin. To obtain good health and well-being, the research of discovery of new drugs for anti-thrombotic still continue. Objective(s): This study aims to predict the potential of 17 compounds owned by the vanillin analog to COX-1 receptor using in silico. Method(s): This research employed a molecular docking analysis using Toshiba hardware and AutoDock Tools version 1.5.7, ChemDraw Professional 16.0, Discovery Studio, UCSF Chimera software, SWISSADME and pKCSM, a native ligand from COX-1 (PDB ID: 1CQE) was validated. Result(s): The validation result indicated that the RMSD was <2 A. The 4-formyl-2-methoxyphenyl benzoate compound had the lowest binding energy in COX-1 inhibition with a value of-7.70 A. All vanillin derivatives show good intestinal absorption, and the predicted toxicity indicated that they were non-hepatotoxic. All these compounds have the potential to be effective antithrombotic treatments when consumed orally. Conclusion(s): In comparison to other vanillin derivative com-pounds, 4-formyl-2-methoxyphenyl benzoate has the lowest binding energy value;hence, this analog can continue to be synthesized and its potential as an antithrombotic agent might be confirmed by in vivo studies.Copyright © the Author(s), 2023.

Discovery and development of lead compounds from natural sources using computational approaches

Natural products have a significant role in drug discovery. Their unique chemical structures have led to compounds in clinical use to treat different diseases. Also, natural products are significant sources of inspiration or starting points to develop new therapeutic agents. There are also unique natural products such as peptides and macrocycles that offer sources or starting points to address complex diseases. Computational approaches that used chemoinformatics and molecular modeling methods contribute to assisting and accelerating natural product-based drug discovery. Several research groups have recently used computational methodologies to organize data, interpret results, generate and test hypotheses, filter large chemical databases before the experimental screening, and design experiments. Herein, we discuss chemoinformatics and molecular modeling applications to uncover bioactive natural products. We also discuss in silico methods to optimize the biological activity and anticipate potential toxicity issues of natural products. As case studies, we discuss the role of natural products for COVID-19 drug discovery and their impact on the identification of compounds with activity against DNA methyltransferase, an epigenetic target with relevance in cancer and other diseases. © 2022 Elsevier Inc. All rights reserved.

Chemoinformatics and bioinformatics by discrete mathematics and numbers: an adventure from small data to the realm of emerging big data

Currently, we are witnessing the emergence of big data in various fields including the biomedical and natural sciences. The size of chemoinformatics and bioinformatics databases is increasing every day. This gives us both challenges and opportunities. This chapter discusses the mathematical methods used in these fields both for the generation and analysis of such data. It is emphasized that proper use of robust statistical and machine learning methods in the analysis of the available big data may facilitate both hypothesis-driven and discovery-oriented research. © 2023 Elsevier Inc. All rights reserved.

Chemoinformatics and Machine Learning Approaches for Identifying Antiviral Compounds.

Current pandemics propelled research efforts in unprecedented fashion, primarily triggering computational efforts towards new vaccine and drug development as well as drug repurposing. There is an urgent need to design novel drugs with targeted biological activity and minimum adverse reactions that may be useful to manage viral outbreaks. Hence an attempt has been made to develop Machine Learning based predictive models that can be used to assess whether a compound has the potency to be antiviral or not. To this end, a set of 2358 antiviral compounds were compiled from the CAS COVID-19 antiviral SAR dataset whose activity was reported based on IC50 value. A total 1157 two-dimensional molecular descriptors were computed among which, the most highly correlated descriptors were selected using Tree-based, Correlation-based and Mutual information-based feature selection methods. Seven Machine Learning algorithms i. e., Random Forest, XGBoost, Support Vector Machine, KNN, Decision Tree, MLP Classifier and Logistic Regression were benchmarked. The best performance was achieved by the models developed using Random Forest and XGBoost algorithms in all the feature selection methods. The maximum predictive accuracy of both these models was 88 % with internal validation. Whereas, with an external dataset, a maximum accuracy of 93.10 % for XGBoost and 100 % for Random Forest based model was achievable. Furthermore, the study demonstrated scaffold analysis of the molecules as a pragmatic approach to explore the importance of structurally diverse compounds in data driven studies.

Identifying FDA-approved drugs with multimodal properties against COVID-19 using a data-driven approach and a lung organoid model of SARS-CoV-2 entry.

BACKGROUND: Vaccination programs have been launched worldwide to halt the spread of COVID-19. However, the identification of existing, safe compounds with combined treatment and prophylactic properties would be beneficial to individuals who are waiting to be vaccinated, particularly in less economically developed countries, where vaccine availability may be initially limited. METHODS: We used a data-driven approach, combining results from the screening of a large transcriptomic database (L1000) and molecular docking analyses, with in vitro tests using a lung organoid model of SARS-CoV-2 entry, to identify drugs with putative multimodal properties against COVID-19. RESULTS: Out of thousands of FDA-approved drugs considered, we observed that atorvastatin was the most promising candidate, as its effects negatively correlated with the transcriptional changes associated with infection. Atorvastatin was further predicted to bind to SARS-CoV-2's main protease and RNA-dependent RNA polymerase, and was shown to inhibit viral entry in our lung organoid model. CONCLUSIONS: Small clinical studies reported that general statin use, and specifically, atorvastatin use, are associated with protective effects against COVID-19. Our study corroborrates these findings and supports the investigation of atorvastatin in larger clinical studies. Ultimately, our framework demonstrates one promising way to fast-track the identification of compounds for COVID-19, which could similarly be applied when tackling future pandemics.

Chemoinformatic Analysis of Psychotropic and Antihistaminic Drugs in the Light of Experimental Anti-SARS-CoV-2 Activities.

INTRODUCTION: There is an urgent need to identify therapies that prevent SARS-CoV-2 infection and improve the outcome of COVID-19 patients. OBJECTIVE: Based upon clinical observations, we proposed that some psychotropic and antihistaminic drugs could protect psychiatric patients from SARS-CoV-2 infection. This observation is investigated in the light of experimental in vitro data on SARS-CoV-2. METHODS: SARS-CoV-2 high-throughput screening results are available at the NCATS COVID-19 portal. We investigated the in vitro anti-viral activity of many psychotropic and antihistaminic drugs using chemoinformatics approaches. RESULTS AND DISCUSSION: We analyze our clinical observations in the light of SARS-CoV-2 experimental screening results and propose that several cationic amphiphilic psychotropic and antihistaminic drugs could protect people from SARS-CoV-2 infection; some of these molecules have very limited adverse effects and could be used as prophylactic drugs. Other cationic amphiphilic drugs used in other disease areas are also highlighted. Recent analyses of patient electronic health records reported by several research groups indicate that some of these molecules could be of interest at different stages of the disease progression. In addition, recently reported drug combination studies further suggest that it might be valuable to associate several cationic amphiphilic drugs. Taken together, these observations underline the need for clinical trials to fully evaluate the potentials of these molecules, some fitting in the so-called category of broad-spectrum antiviral agents. Repositioning orally available drugs that have moderate side effects and should act on molecular mechanisms less prone to drug resistance would indeed be of utmost importance to deal with COVID-19.

In Silico Screening of Natural Products Isolated from Mexican Herbal Medicines against COVID-19.

The COVID-19 pandemic has already taken the lives of more than 2 million people worldwide, causing several political and socio-economic disturbances in our daily life. At the time of publication, there are non-effective pharmacological treatments, and vaccine distribution represents an important challenge for all countries. In this sense, research for novel molecules becomes essential to develop treatments against the SARS-CoV-2 virus. In this context, Mexican natural products have proven to be quite useful for drug development; therefore, in the present study, we perform an in silico screening of 100 compounds isolated from the most commonly used Mexican plants, against the SARS-CoV-2 virus. As results, we identify ten compounds that meet leadlikeness criteria (emodin anthrone, kaempferol, quercetin, aesculin, cichoriin, luteolin, matricin, riolozatrione, monocaffeoyl tartaric acid, aucubin). According to the docking analysis, only three compounds target the key proteins of SARS-CoV-2 (quercetin, riolozatrione and cichoriin), but only one appears to be safe (cichoriin). ADME (absorption, distribution, metabolism and excretion) properties and the physiologically based pharmacokinetic (PBPK) model show that cichoriin reaches higher lung levels (100 mg/Kg, IV); therefore, it may be considered in developing therapeutic tools.

Virtual screening of phytoconstituents from miracle herb nigella sativa targeting nucleocapsid protein and papain-like protease of SARS-CoV-2 for COVID-19 treatment.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a novel etiological agent of coronavirus disease 2019 (COVID-19). Nigella sativa, commonly known as black seed or black cumin, has been a historical and traditional plant since thousands of years. Based on their therapeutic efficacy, the chief components of terpenoids and flavonoids were selected from N. sativa seeds and seed oil. This study was designed to check the antiviral efficacy of N. sativa main phytoconstituents against five potential targets of SARS-CoV-2 using in silico structure-based virtual screening approach. Out of twenty five phytocomponents, ten components showed best binding affinity against two viral proteins viz. N-terminal RNA binding domain (NRBD; PDB ID: 6M3M) of nucleocapsid protein and papain-like protease (PL-PRO; PDB ID: 6W9C) of SARS-CoV-2 using AutoDock 4.2.6, AutoDock Vina and iGEMDOCK. PASS analyses of all ten phytocomponents using Lipinski's Rule of five showed promising results. Further, druglikeness and toxicity assessment using OSIRIS Data Warrior v5.2.1 software exhibited the feasibility of phytocomponents as drug candidates with no predicted toxicity. Molecular dynamics simulation study of NRBD of SARS-CoV-2 nucleocapsid protein-alpha-spinasterol complex and PL-PRO-cycloeucalenol complex displayed strong stability at 300 K. Both these complexes exhibited constant root mean square deviation (RMSDs) of protein side chains and Cα atoms throughout the simulation run time. Interestingly, PL-PRO and NRBD are key proteins in viral replication, host cell immune evasion and viral assembly. Thus, NRBD and PL-PRO have the potential to serve as therapeutic targets for N. sativa phytoconstituents in drug discovery process against COVID-19.

Exploring nature's bounty: identification of Withania somnifera as a promising source of therapeutic agents against COVID-19 by virtual screening and in silico evaluation.

Coronaviruses are etiological agents of extreme human and animal infection resulting in abnormalities primarily in the respiratory tract. Presently, there is no defined COVID-19 intervention and clinical trials of prospective therapeutic agents are still in the nascent stage. Withania somnifera (L.) Dunal (WS), is an important medicinal plant in Ayurveda. The present study aimed to evaluate the antiviral potential of selected WS phytoconstituents against the novel SARS-CoV-2 target proteins and human ACE2 receptor using in silico methods. Most of the phytoconstituents displayed good absorption and transport kinetics and were also found to display no associated mutagenic or adverse effect(s). Molecular docking analyses revealed that most of the WS phytoconstituents exhibited potent binding to human ACE2 receptor, SAR-CoV and SARS-CoV-2 spike glycoproteins as well as the two main SARS-CoV-2 proteases. Most of the phytoconstituents were predicted to undergo Phase-I metabolism prior to excretion. All phytoconstituents had favorable bioactivity scores with respect to various receptor proteins and target enzymes. SAR analysis revealed that the number of oxygen atoms in the withanolide backbone and structural rearrangements were crucial for effective binding. Molecular simulation analyses of SARS-CoV-2 spike protein and papain-like protease with Withanolides A and B, respectively, displayed a stability profile at 300 K and constant RMSDs of protein side chains and Cα atoms throughout the simulation run time. In a nutshell, WS phytoconstituents warrant further investigations in vitro and in vivo to unravel their molecular mechanism(s) and modes of action for their future development as novel antiviral agents against COVID-19.

Fragment Library of Natural Products and Compound Databases for Drug Discovery.

Natural products and semi-synthetic compounds continue to be a significant source of drug candidates for a broad range of diseases, including coronavirus disease 2019 (COVID-19), which is causing the current pandemic. Besides being attractive sources of bioactive compounds for further development or optimization, natural products are excellent substrates of unique substructures for fragment-based drug discovery. To this end, fragment libraries should be incorporated into automated drug design pipelines. However, public fragment libraries based on extensive collections of natural products are still limited. Herein, we report the generation and analysis of a fragment library of natural products derived from a database with more than 400,000 compounds. We also report fragment libraries of a large food chemical database and other compound datasets of interest in drug discovery, including compound libraries relevant for COVID-19 drug discovery. The fragment libraries were characterized in terms of content and diversity.