Consensus Pharmacophore Strategy For Identifying Novel SARS-Cov-2 Mpro Inhibitors from Large Chemical Libraries

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main Protease (Mpro) is an enzyme that cleaves viral polyproteins translated from the viral genome and is critical for viral replication. Mpro is a target for anti-SARS-CoV-2 drug development, and multiple Mpro crystals complexed with competitive inhibitors have been reported. In this study, we aimed to develop an Mpro consensus pharmacophore as a tool to expand the search for inhibitors. We generated a consensus model by aligning and summarizing pharmacophoric points from 152 bioactive conformers of SARS-CoV-2 Mpro inhibitors. Validation against a library of conformers from a subset of ligands showed that our model retrieved poses that reproduced the crystal-binding mode in 77% of the cases. Using models derived from a consensus pharmacophore, we screened >340 million compounds. Pharmacophore-matching and chemoinformatics analyses identified new potential Mpro inhibitors. The candidate compounds were chemically dissimilar to the reference set, and among them, demonstrating the relevance of our model. We evaluated the effect of 16 candidates on Mpro enzymatic activity finding that seven have inhibitory activity. Three compounds (1, 4, and 5) had IC50 values in the midmicromolar range. The Mpro consensus pharmacophore reported herein can be used to identify compounds with improved activity and novel chemical scaffolds against Mpro. The method developed for its generation is provided as an open-access code (https://github.com/AngelRuizMoreno/ConcensusPharmacophore) and can be applied to other pharmacological targets.

Chemical Multiverse and Diversity of Food Chemicals

Food chemicals have a fundamental role in our lives, with an extended impact on nutrition, disease prevention, and marked economic implications in the food industry. The number of food chemical compounds in public databases has substantially increased in the past few years, which can be characterized using chemoinformatics approaches. We and other groups explored public food chemical libraries containing up to 26,500 compounds. This study aimed to analyze the chemical contents, diversity, and coverage in the chemical space of food chemicals and additives and, from here on, food components. The approach to food components addressed in this study is a public database with more than 70,000 compounds, including those predicted via omics techniques. It was concluded that food components have distinctive physicochemical properties and constitutional descriptors despite sharing many chemical structures with natural products. Food components, on average, have large molecular weights and several apolar structures with saturated hydrocarbons. Compared to reference databases, food component structures have low scaffold and fingerprint-based diversity and high structural complexity, as measured by the fraction of sp3 carbons. These structural features are associated with a large fraction of macronutrients as lipids. Lipids in food components were decompiled by an analysis of the maximum common substructures. The chemical multiverse representation of food chemicals showed a larger coverage of chemical space than natural products and FDA-approved drugs by using different sets of representations.

Identification of SARS-CoV-2 Main Protease Inhibitors Using Chemical Similarity Analysis Combined with Machine Learning

SARS-CoV-2 Main Protease (Mpro) is an enzyme that cleaves viral polyproteins translated from the viral genome, which is critical for viral replication. Mpro is a target for anti-SARS-CoV-2 drug development. Herein, we performed a large-scale virtual screening by comparing multiple structural descriptors of reference molecules with reported anti-coronavirus activity against a library with >17 million compounds. Further filtering, performed by applying two machine learning algorithms, identified eighteen computational hits as anti-SARS-CoV-2 compounds with high structural diversity and drug-like properties. The activities of twelve compounds on Mpro's enzymatic activity were evaluated by fluorescence resonance energy transfer (FRET) assays. Compound 13 (ZINC13878776) significantly inhibited SARS-CoV-2 Mpro activity and was employed as a reference for an experimentally hit expansion. The structural analogues 13a (ZINC4248385), 13b (ZNC13523222), and 13c (ZINC4248365) were tested as Mpro inhibitors, reducing the enzymatic activity of recombinant Mpro with potency as follows: 13c > 13 > 13b > 13a. Then, their anti-SARS-CoV-2 activities were evaluated in plaque reduction assays using Vero CCL81 cells. Subtoxic concentrations of compounds 13a, 13c, and 13b displayed in vitro antiviral activity with IC50 in the mid micromolar range. Compounds 13a-c could become lead compounds for the development of new Mpro inhibitors with improved activity against anti-SARS-CoV-2.

The pursuit of accurate predictive models of the bioactivity of small molecules

Property prediction is a key interest in chemistry. For several decades there has been a continued and incremental development of mathematical models to predict properties. As more data is generated and accumulated, there seems to be more areas of opportunity to develop models with increased accuracy. The same is true if one considers the large developments in machine and deep learning models. However, along with the same areas of opportunity and development, issues and challenges remain and, with more data, new challenges emerge such as the quality and quantity and reliability of the data, and model reproducibility. Herein, we discuss the status of the accuracy of predictive models and present the authors' perspective of the direction of the field, emphasizing on good practices. We focus on predictive models of bioactive properties of small molecules relevant for drug discovery, agrochemical, food chemistry, natural product research, and related fields.

Design of a multi-target focused library for antidiabetic targets using a comprehensive set of chemical transformation rules

Virtual small molecule libraries are valuable resources for identifying bioactive compounds in virtual screening campaigns and improving the quality of libraries in terms of physicochemical properties, complexity, and structural diversity. In this context, the computational-aided design of libraries focused against antidiabetic targets can provide novel alternatives for treating type II diabetes mellitus (T2DM). In this work, we integrated the information generated to date on compounds with antidiabetic activity, advances in computational methods, and knowledge of chemical transformations available in the literature to design multi-target compound libraries focused on T2DM. We evaluated the novelty and diversity of the newly generated library by comparing it with antidiabetic compounds approved for clinical use, natural products, and multi-target compounds tested in vivo in experimental antidiabetic models. The designed libraries are freely available and are a valuable starting point for drug design, chemical synthesis, and biological evaluation or further computational filtering. Also, the compendium of 280 transformation rules identified in a medicinal chemistry context is made available in the linear notation SMIRKS for use in other chemical library enumeration or hit optimization approaches.

Anthocyanins: Molecular Aspects on Their Neuroprotective Activity

Anthocyanins are a type of flavonoids that give plants and fruits their vibrant colors. They are known for their potent antioxidant properties and have been linked to various health benefits. Upon consumption, anthocyanins are quickly absorbed and can penetrate the blood-brain barrier (BBB). Research based on population studies suggests that including anthocyanin-rich sources in the diet lower the risk of neurodegenerative diseases. Anthocyanins exhibit neuroprotective effects that could potentially alleviate symptoms associated with such diseases. In this review, we compiled and discussed a large body of evidence supporting the neuroprotective role of anthocyanins. Our examination encompasses human studies, animal models, and cell cultures. We delve into the connection between anthocyanin bioactivities and the mechanisms underlying neurodegeneration. Our findings highlight how anthocyanins' antioxidant, anti-inflammatory, and anti-apoptotic properties contribute to their neuroprotective effects. These effects are particularly relevant to key signaling pathways implicated in the development of Alzheimer's and Parkinson's diseases. In conclusion, the outcome of this review suggests that integrating anthocyanin-rich foods into human diets could potentially serve as a therapeutic approach for neurological conditions, and we identify promising avenues for further exploration in this area.

Challenges in natural product-based drug discovery assisted with in silico-based methods

The application of traditional medicine by humans for the treatment of ailments as well as improving the quality of life far outdates recorded history. To date, a significant percentage of humans, especially those living in developing/underprivileged communities still rely on traditional medicine for primary healthcare needs. In silico-based methods have been shown to play a pivotal role in modern pharmaceutical drug discovery processes. The application of these methods in identifying natural product (NP)-based hits has been successful. This is very much observed in many research set-ups that use rationally in silico-based methods in combination with experimental validation techniques. The combination has rendered the use of in silico-based approaches even more popular and successful in the investigation of NPs. However, identifying and proposing novel NP-based hits for experimental validation comes with several challenges such as the availability of compounds by suppliers, the huge task of separating pure compounds from complex mixtures, the quantity of samples available from the natural source to be tested, not to mention the potential ecological impact if the natural source is exhausted. Because most peer-reviewed publications are biased towards "positive results", these challenges are generally not discussed in publications. In this review, we highlight and discuss these challenges. The idea is to give interested scientists in this field of research an idea of what they can come across or should be expecting as well as prompting them on how to avoid or fix these issues.

Art driven by visual representations of chemical space

Science and art have been connected for centuries. With the development of new computational methods, new scientific disciplines have emerged, such as computational chemistry, and related fields, such as cheminformatics. Chemoinformatics is grounded on the chemical space concept: a multi-descriptor space in which chemical structures are described. In several practical applications, visual representations of the chemical space of compound datasets are low-dimensional plots helpful in identifying patterns. However, the authors propose that the plots can also be used as artistic expressions. This manuscript introduces an approach to merging art with chemoinformatics through visual and artistic representations of chemical space. As case studies, we portray the chemical space of food chemicals and other compounds to generate visually appealing graphs with twofold benefits: sharing chemical knowledge and developing pieces of art driven by chemoinformatics. The art driven by chemical space visualization will help increase the application of chemistry and art and contribute to general education and dissemination of chemoinformatics and chemistry through artistic expressions. All the code and data sets to reproduce the visual representation of the chemical space presented in the manuscript are freely available at https://github.com/DIFACQUIM/Art-Driven-by-Visual-Representations-of-Chemical-Space- . Scientific contribution: Chemical space as a concept to create digital art and as a tool to train and introduce students to cheminformatics.

Navigating the Chemical Space and Chemical Multiverse of a Unified Latin American Natural Product Database: LANaPDB

The number of databases of natural products (NPs) has increased substantially. Latin America is extraordinarily rich in biodiversity, enabling the identification of novel NPs, which has encouraged both the development of databases and the implementation of those that are being created or are under development. In a collective effort from several Latin American countries, herein we introduce the first version of the Latin American Natural Products Database (LANaPDB), a public compound collection that gathers the chemical information of NPs contained in diverse databases from this geographical region. The current version of LANaPDB unifies the information from six countries and contains 12,959 chemical structures. The structural classification showed that the most abundant compounds are the terpenoids (63.2%), phenylpropanoids (18%) and alkaloids (11.8%). From the analysis of the distribution of properties of pharmaceutical interest, it was observed that many LANaPDB compounds satisfy some drug-like rules of thumb for physicochemical properties. The concept of the chemical multiverse was employed to generate multiple chemical spaces from two different fingerprints and two dimensionality reduction techniques. Comparing LANaPDB with FDA-approved drugs and the major open-access repository of NPs, COCONUT, it was concluded that the chemical space covered by LANaPDB completely overlaps with COCONUT and, in some regions, with FDA-approved drugs. LANaPDB will be updated, adding more compounds from each database, plus the addition of databases from other Latin American countries.

School of cheminformatics in Latin America

We report the major highlights of the School of Cheminformatics in Latin America, Mexico City, November 24-25, 2022. Six lectures, one workshop, and one roundtable with four editors were presented during an online public event with speakers from academia, big pharma, and public research institutions. One thousand one hundred eighty-one students and academics from seventy-nine countries registered for the meeting. As part of the meeting, advances in enumeration and visualization of chemical space, applications in natural product-based drug discovery, drug discovery for neglected diseases, toxicity prediction, and general guidelines for data analysis were discussed. Experts from ChEMBL presented a workshop on how to use the resources of this major compounds database used in cheminformatics. The school also included a round table with editors of cheminformatics journals. The full program of the meeting and the recordings of the sessions are publicly available at https://www.youtube.com/@SchoolChemInfLA/featured .

Sampling and Mapping Chemical Space with Extended Similarity Indices

Visualization of the chemical space is useful in many aspects of chemistry, including compound library design, diversity analysis, and exploring structure-property relationships, to name a few. Examples of notable research areas where the visualization of chemical space has strong applications are drug discovery and natural product research. However, the sheer volume of even comparatively small sub-sections of chemical space implies that we need to use approximations at the time of navigating through chemical space. ChemMaps is a visualization methodology that approximates the distribution of compounds in large datasets based on the selection of satellite compounds that yield a similar mapping of the whole dataset when principal component analysis on a similarity matrix is performed. Here, we show how the recently proposed extended similarity indices can help find regions that are relevant to sample satellites and reduce the amount of high-dimensional data needed to describe a library's chemical space.

Diversity and Chemical Space Characterization of Inhibitors of the Epigenetic Target G9a: A Chemoinformatics Approach

G9a is a histone-lysine methyltransferase that performs the mono- and dimethylation of lysine 9 at histone 3 of the nucleosome. It belongs to the SET PKMT family, and its methylations are related to promoter repression and activation. G9a is a promising epigenetic target. Despite the fact that there are several G9a inhibitors under development, there are no compounds in clinical use due to adverse in vivo ADMET (absorption, distribution, metabolism, excretion, and toxicity) issues. The goal of this study is to discuss the exploration, characterization, and analysis of the chemical space of 409 G9a inhibitors reported in a large public database. Exploring the chemical space of the inhibitors led to the quantification of their structural diversity based on molecular scaffolds and structural fingerprints of different designs. As part of the analysis, the G9a inhibitors were compared with commercial libraries focused on epigenetic targets. The findings of this work will help in the development of, in a follow-up study, predictive models to identify G9a inhibitors. This study also points out the relevance of screening commercial libraries to expand the epigenetic relevant chemical space, in particular, G9a inhibitors.

Toward α-1,3/4 fucosyltransferases targeted drug discovery: In silico uncovering of promising natural inhibitors of fucosyltransferase 6

Sialyl Lewis X (sLex ) antigen is a fucosylated cell-surface glycan that is normally involved in cell-cell interactions. The enhanced expression of sLex on cell surface glycans, which is attributed to the upregulation of fucosyltransferase 6 (FUT6), has been implicated in facilitating metastasis in human colorectal, lung, prostate, and oral cancers. The role that the upregulated FUT6 plays in the progression of tumor to malignancy, with reduced survival rates, makes it a potential target for anticancer drugs. Unfortunately, the lack of experimental structures for FUT6 has hampered the design and development of its inhibitors. In this study, we used in silico techniques to identify potential FUT6 inhibitors. We first modeled the three-dimensional structure of human FUT6 using AlphaFold. Then, we screened the natural compound libraries from the COCONUT database to sort out potential natural products (NPs) with best affinity toward the FUT6 model. As a result of these simulations, we identified three NPs for which we predicted binding affinities and interaction patterns quite similar to those we calculated for two experimentally tested FUT6 inhibitors, that is, fucose mimetic-1 and a GDP-triazole derived compound. We also performed molecular dynamics (MD) simulations for the FUT6 complexes with identified NPs, to investigate their stability. Analysis of the MD simulations showed that the identified NPs establish stable contacts with FUT6 under dynamics conditions. On these grounds, the three screened compounds appear as promising natural alternatives to experimentally tested FUT6 synthetic inhibitors, with expected comparable binding affinity. This envisages good prospects for future experimental validation toward FUT6 inhibition.

Design and Diversity Analysis of Chemical Libraries in Drug Discovery

Chemical libraries and compound data sets are among the main inputs to start the drug discovery process at universities, research institutes, and the pharmaceutical industry. The approach used in the design of compound libraries, the chemical information they possess, and the representation of structures, play a fundamental role in the development of studies: chemoinformatics, food informatics, in silico pharmacokinetics, computational toxicology, bioinformatics, and molecular modeling to generate computational hits that will continue the optimization process of drug candidates. The prospects for growth in drug discovery and development processes in chemical, biotechnological, and pharmaceutical companies began a few years ago by integrating computational tools with artificial intelligence methodologies. It is anticipated that it will increase the number of drugs approved by regulatory agencies shortly.

PeruNPDB: the Peruvian Natural Products Database for in silico drug screening

Since the number of drugs based on natural products (NPs) represents a large source of novel pharmacological entities, NPs have acquired significance in drug discovery. Peru is considered a megadiverse country with many endemic species of plants, terrestrial, and marine animals, and microorganisms. NPs databases have a major impact on drug discovery development. For this reason, several countries such as Mexico, Brazil, India, and China have initiatives to assemble and maintain NPs databases that are representative of their diversity and ethnopharmacological usage. We describe the assembly, curation, and chemoinformatic evaluation of the content and coverage in chemical space, as well as the physicochemical attributes and chemical diversity of the initial version of the Peruvian Natural Products Database (PeruNPDB), which contains 280 natural products. Access to PeruNPDB is available for free ( https://perunpdb.com.pe/ ). The PeruNPDB's collection is intended to be used in a variety of tasks, such as virtual screening campaigns against various disease targets or biological endpoints. This emphasizes the significance of biodiversity protection both directly and indirectly on human health.

Towards Decoding Hepatotoxicity of Approved Drugs through Navigation of Multiverse and Consensus Chemical Spaces

Drug-induced liver injury (DILI) is the principal reason for failure in developing drug candidates. It is the most common reason to withdraw from the market after a drug has been approved for clinical use. In this context, data from animal models, liver function tests, and chemical properties could complement each other to understand DILI events better and prevent them. Since the chemical space concept improves decision-making drug design related to the prediction of structure-property relationships, side effects, and polypharmacology drug activity (uniquely mentioning the most recent advances), it is an attractive approach to combining different phenomena influencing DILI events (e.g., individual "chemical spaces") and exploring all events simultaneously in an integrated analysis of the DILI-relevant chemical space. However, currently, no systematic methods allow the fusion of a collection of different chemical spaces to collect different types of data on a unique chemical space representation, namely "consensus chemical space." This study is the first report that implements data fusion to consider different criteria simultaneously to facilitate the analysis of DILI-related events. In particular, the study highlights the importance of analyzing together in vitro and chemical data (e.g., topology, bond order, atom types, presence of rings, ring sizes, and aromaticity of compounds encoded on RDKit fingerprints). These properties could be aimed at improving the understanding of DILI events.

Consensus Virtual Screening Protocol Towards the Identification of Small Molecules Interacting with the Colchicine Binding Site of the Tubulin-microtubule System

Modification of the tubulin-microtubule (Tub-Mts) system has generated effective strategies for developing different treatments for cancer. A huge amount of clinical data about inhibitors of the tubulin-microtubule system have supported and validated the studies on this pharmacological target. However, many tubulin-microtubule inhibitors have been developed from representative and common scaffolds that cover a small region of the chemical space with limited structural innovation. The main goal of this study is to develop the first consensus virtual screening protocol for natural products (ligand- and structure-based drug design methods) tuned for the identification of new potential inhibitors of the Tub-Mts system. A combined strategy that involves molecular similarity, molecular docking, pharmacophore modeling, and in silico ADMET prediction has been employed to prioritize the selections of potential inhibitors of the Tub-Mts system. Five compounds were selected and further studied using molecular dynamics and binding energy predictions to characterize their possible binding mechanisms. Their structures correspond to 5-[2-(4-hydroxy-3-methoxyphenyl) ethyl]-2,3-dimethoxyphenol (1), 9,10-dihydro-3,4-dimethoxy-2,7-phenanthrenediol (2), 2-(3,4-dimethoxyphenyl)-5,7-dihydroxy-6-methoxy-4H-1-benzopyran-4-one (3), 13,14-epoxyparvifoline-4',5',6'-trimethoxybenzoate (4), and phenylmethyl 6-hydroxy-2,3-dimethoxybenzoate (5). Compounds 1-3 have been associated with literature reports that confirm their activity against several cancer cell lines, thus supporting the utility of this protocol.

Chemoinformatics and artificial intelligence colloquium: progress and challenges in developing bioactive compounds

We report the main conclusions of the first Chemoinformatics and Artificial Intelligence Colloquium, Mexico City, June 15-17, 2022. Fifteen lectures were presented during a virtual public event with speakers from industry, academia, and non-for-profit organizations. Twelve hundred and ninety students and academics from more than 60 countries. During the meeting, applications, challenges, and opportunities in drug discovery, de novo drug design, ADME-Tox (absorption, distribution, metabolism, excretion and toxicity) property predictions, organic chemistry, peptides, and antibiotic resistance were discussed. The program along with the recordings of all sessions are freely available at https://www.difacquim.com/english/events/2022-colloquium/ .

Potential Anticancer Activity of Pomegranate (Punica granatum L.) Fruits of Different Color: In Vitro and In Silico Evidence

Pomegranate (PMG; Punica granatum L.) fruits possess a well-balanced nutrient/phytochemical composition, with proven adjuvant benefits in experimental cancer chemotherapy; however, such bioactivity could be affected by PMG's phenogenotype (varietal). Here, the chemical and phytochemical (UPLC-DAD-MS2) composition, antioxidant capacity and anticancer potential [in vitro (MTT assay) and in silico (foodinformatics)] of three PMG fruits of different aryl color [red (cv. Wonderful), pink (cv. Molar de Elche), and white (cv. Indian)] were evaluated. The macro/micronutrient (ascorbic acid, tocols, carotenoids), organic acid (citric/malic), and polyphenol content were changed by PMG's varietal and total antioxidant activity (ABTS, alcoholic > hexane extract) in the order of red > pink > white. However, their in vitro cytotoxicity was the same (IC50 > 200 μg.mL-1) against normal (retinal) and cancer (breast, lung, colorectal) cell lines. Sixteen major phytochemicals were tentatively identified, four of them with a high GI absorption/bioavailability score [Ellagic (pink), vanillic (red), gallic (white) acids, D-(+)-catechin (white)] and three of them with multiple molecular targets [Ellagic (52) > vanillic (32) > gallic (23)] associated with anticancer (at initiation and promotion stages) activity. The anticancer potential of the PMG fruit is phenogenotype-specific, although it could be more effective in nutraceutical formulations (concentrates).

Approaches for enhancing the analysis of chemical space for drug discovery

INTRODUCTION

Chemical space is a powerful, general, and practical conceptual framework in drug discovery and other areas in chemistry that addresses the diversity of molecules and it has various applications. Moreover, chemical space is a cornerstone of chemoinformatics as a scientific discipline. In response to the increase in the set of chemical compounds in databases, generators of chemical structures, and tools to calculate molecular descriptors, novel approaches to generate visual representations of chemical space in low dimensions are emerging and evolving. Such approaches include a wide range of commercial and free applications, software, and open-source methods.

AREAS COVERED

The current state of chemical space in drug design and discovery is reviewed. The topics discussed herein include advances for efficient navigation in chemical space, the use of this concept in assessing the diversity of different data sets, exploring structure-property/activity relationships for one or multiple endpoints, and compound library design. Recent advances in methodologies for generating visual representations of chemical space have been highlighted, thereby emphasizing open-source methods.

EXPERT OPINION

Quantitative and qualitative generation and analysis of chemical space require novel approaches for handling the increasing number of molecules and their information available in chemical databases (including emerging ultra-large libraries). In addition, it is of utmost importance to note that chemical space is a conceptual framework that goes beyond visual representation in low dimensions. However, the graphical representation of chemical space has several practical applications in drug discovery and beyond.

Yes SIR! On the structure-inactivity relationships in drug discovery

In analogy with structure-activity relationships (SARs), which are at the core of medicinal chemistry, studying structure-inactivity relationships (SIRs) is essential to understanding and predicting biological activity. Current computational methods should predict or distinguish 'activity' and 'inactivity' with the same confidence because both concepts are complementary. However, the lack of inactivity data, in particular in the public domain, limits the development of predictive models and its broad application. In this review, we encourage the scientific community to disclose and analyze high-confidence activity data considering both the labeled 'active' and 'inactive' compounds.

Methyl benzoate and cinnamate analogs as modulators of DNA methylation in hepatocellular carcinoma

Phenolic acids represent a large collection of phytochemical molecules present in the plant kingdom; they have an important role as epigenetic regulators, particularly as inhibitors of DNA methylation. In the present study, 14 methyl benzoate and cinnamate analogs were synthesized (11-24). Their cytotoxic activity on hepatocellular carcinoma cells (Hep3B) and immortalized human hepatocyte cells was then evaluated. In addition, its effect on the inhibition of global DNA methylation in Hep3B was also determined. Our results showed that the cinnamic derivatives 11-14 and 20-22 were more potent than the free caffeic acid (IC₅₀ 109.7-364.2 µM), being methyl 3,4-dihydroxycinammate (12) the most active with an IC₅₀  = 109.7 ± 0.8 µM. Furthermore, 11-14, 20-23 compounds decreased overall DNA methylation levels by 63% to 97%. The analogs methyl 4-hydroxycinnamate (11), methyl 3,4,5-trimethoxycinnamate (14), methyl 4-methoxycinnamate (21), and methyl 3,4-dimethoxycinnamate (22) showed relevant activities of both cytotoxicity and global DNA methylation inhibition. The molecular docking of 21 and 14 suggested that they partly bind to the SAH-binding pocket of DNA methyltransferase 1. These results emphasize the importance of natural products and their analogs as potential sources of DNA methylation modulating agents.

Natural product drug discovery in the artificial intelligence era

Natural products (NPs) are primarily recognized as privileged structures to interact with protein drug targets. Their unique characteristics and structural diversity continue to marvel scientists for developing NP-inspired medicines, even though the pharmaceutical industry has largely given up. High-performance computer hardware, extensive storage, accessible software and affordable online education have democratized the use of artificial intelligence (AI) in many sectors and research areas. The last decades have introduced natural language processing and machine learning algorithms, two subfields of AI, to tackle NP drug discovery challenges and open up opportunities. In this article, we review and discuss the rational applications of AI approaches developed to assist in discovering bioactive NPs and capturing the molecular "patterns" of these privileged structures for combinatorial design or target selectivity.

Cheminformatics analysis of molecular datasets of transcription factors associated with quorum sensing in Pseudomonas aeruginosa

There are molecular structural features that are key to defining the agonist or antagonist activity on LasR, RhlR and PqsR transcription factors, associated with quorum sensing in Pseudomonas aeruginosa.

Chemoinformatic Characterization of Synthetic Screening Libraries Focused on Epigenetic Targets

The importance of epigenetic drug and probe discovery is on the rise. This is not only paramount to identify and develop therapeutic treatments associated with epigenetic processes but also to understand the underlying epigenetic mechanisms involved in biological processes. To this end, chemical vendors have been developing synthetic compound libraries focused on epigenetic targets to increase the probabilities of identifying promising starting points for drug or probe candidates. However, the chemical contents of these data sets, the distribution of their physicochemical properties, and diversity remain unknown. To fill this gap and make this information available to the scientific community, we report a comprehensive analysis of eleven libraries focused on epigenetic targets containing more than 50,000 compounds. We used well-validated chemoinformatics approaches to characterize these sets, including novel methods such as automated detection of analog series and visual representations of the chemical space based on Constellation Plots and Chemical Library Networks. This work will guide the efforts of experimental groups working on high-throughput and medium-throughput screening of epigenetic-focused libraries. The outcome of this work can also be used as a reference to design and describe novel focused epigenetic libraries.

Towards the De Novo Design of HIV-1 Protease Inhibitors Based on Natural Products

Acquired immunodeficiency syndrome (AIDS) caused by the human immunodeficiency virus (HIV) continues to be a public health problem. In 2020, 680,000 people died from HIV-related causes, and 1.5 million people were infected. Antiretrovirals are a way to control HIV infection but not to cure AIDS. As such, effective treatment must be developed to control AIDS. Developing a drug is not an easy task, and there is an enormous amount of work and economic resources invested. For this reason, it is highly convenient to employ computer-aided drug design methods, which can help generate and identify novel molecules. Using the de novo design, novel molecules can be developed using fragments as building blocks. In this work, we develop a virtual focused compound library of HIV-1 viral protease inhibitors from natural product fragments. Natural products are characterized by a large diversity of functional groups, many sp³ atoms, and chiral centers. Pseudo-natural products are a combination of natural products fragments that keep the desired structural characteristics from different natural products. An interactive version of chemical space visualization of virtual compounds focused on HIV-1 viral protease inhibitors from natural product fragments is freely available in the supplementary material.

Diversity and Chemical Library Networks of Large Data Sets

The quantification of chemical diversity has many applications in drug discovery, organic chemistry, food, and natural product chemistry, to name a few. As the size of the chemical space is expanding rapidly, it is imperative to develop efficient methods to quantify the diversity of large and ultralarge chemical libraries and visualize their mutual relationships in chemical space. Herein, we show an application of our recently introduced extended similarity indices to measure the fingerprint-based diversity of 19 chemical libraries typically used in drug discovery and natural products research with over 18 million compounds. Based on this concept, we introduce the Chemical Library Networks (CLNs) as a general and efficient framework to represent visually the chemical space of large chemical libraries providing a global perspective of the relation between the libraries. For the 19 compound libraries explored in this work, it was found that the (extended) Tanimoto index offers the best description of extended similarity in combination with RDKit fingerprints. CLNs are general and can be explored with any structure representation and similarity coefficient for large chemical libraries.

Advances in the Exploration of the Epigenetic Relevant Chemical Space

Epigenetic drug discovery is a promising avenue to find therapeutic agents for treating several diseases and developing novel chemical probes for research. In order to identify hit and lead compounds, the chemical space has been explored and screened, generating valuable bioactivity information that can be used for multiple purposes such as prediction of the activity of existing chemicals, e.g., small molecules, guiding the design or optimization of compounds, and expanding the epigenetic relevant chemical space. Herein, we review the chemical spaces explored for epigenetic drug discovery and discuss the advances in using structure-activity relationships stored in public chemogenomic databases. We also review current efforts to chart and identify novel regions of the epigenetic relevant chemical space. In particular, we discuss the development and accessibility of two significant types of compound libraries focused on epigenetic targets: commercially available libraries for screening and targeted chemical libraries using de novo design. In this mini-review, we emphasize inhibitors of DNA methyltransferases.

Computational Applications in Secondary Metabolite Discovery (CAiSMD): an online workshop

We report the major conclusions of the online open-access workshop "Computational Applications in Secondary Metabolite Discovery (CAiSMD)" that took place from 08 to 10 March 2021. Invited speakers from academia and industry and about 200 registered participants from five continents (Africa, Asia, Europe, South America, and North America) took part in the workshop. The workshop highlighted the potential applications of computational methodologies in the search for secondary metabolites (SMs) or natural products (NPs) as potential drugs and drug leads. During 3 days, the participants of this online workshop received an overview of modern computer-based approaches for exploring NP discovery in the "omics" age. The invited experts gave keynote lectures, trained participants in hands-on sessions, and held round table discussions. This was followed by oral presentations with much interaction between the speakers and the audience. Selected applicants (early-career scientists) were offered the opportunity to give oral presentations (15 min) and present posters in the form of flash presentations (5 min) upon submission of an abstract. The final program available on the workshop website ( https://caismd.indiayouth.info/ ) comprised of 4 keynote lectures (KLs), 12 oral presentations (OPs), 2 round table discussions (RTDs), and 5 hands-on sessions (HSs). This meeting report also references internet resources for computational biology in the area of secondary metabolites that are of use outside of the workshop areas and will constitute a long-term valuable source for the community. The workshop concluded with an online survey form to be completed by speakers and participants for the goal of improving any subsequent editions.

A critical overview of computational approaches employed for COVID-19 drug discovery

COVID-19 has resulted in huge numbers of infections and deaths worldwide and brought the most severe disruptions to societies and economies since the Great Depression. Massive experimental and computational research effort to understand and characterize the disease and rapidly develop diagnostics, vaccines, and drugs has emerged in response to this devastating pandemic and more than 130 000 COVID-19-related research papers have been published in peer-reviewed journals or deposited in preprint servers. Much of the research effort has focused on the discovery of novel drug candidates or repurposing of existing drugs against COVID-19, and many such projects have been either exclusively computational or computer-aided experimental studies. Herein, we provide an expert overview of the key computational methods and their applications for the discovery of COVID-19 small-molecule therapeutics that have been reported in the research literature. We further outline that, after the first year the COVID-19 pandemic, it appears that drug repurposing has not produced rapid and global solutions. However, several known drugs have been used in the clinic to cure COVID-19 patients, and a few repurposed drugs continue to be considered in clinical trials, along with several novel clinical candidates. We posit that truly impactful computational tools must deliver actionable, experimentally testable hypotheses enabling the discovery of novel drugs and drug combinations, and that open science and rapid sharing of research results are critical to accelerate the development of novel, much needed therapeutics for COVID-19.

Chemoinformatic Analysis of Isothiocyanates: Their Impact in Nature and Medicine

Isothiocyanates (ITCs) have a significant impact on food and natural product chemistry. Several dietary components and food chemicals contain the isothiocyanate moiety. In addition, many ITCs interact with macromolecules of biological relevance, making these compounds relevant for potential therapeutic applications and disease prevention. However, there is a lack of systematic analysis of ITCs in chemical and biological databases. Herein, we conducted a comprehensive analysis of ITCs present in public domain databases, including natural products, food chemicals, macromolecular targets of drugs, and the Protein Data Bank. A total of 154 ITCs were found, which can be classified into seven categories: acyclic, cyclic, polycyclic, aromatic, polyaromatic, indolic, and glycosylated. 24 ITCs were reported in 18 vegetable sources, mainly in cruciferous vegetables (Brassica oleracea L.). Calculated properties of pharmaceutical relevance indicated that 11 % of the 154 ITCs would be suitable to be orally absorbed and 48 % permeate the blood-brain-barrier. It was also found that seven molecular targets have been co-crystallized with ITCs and the most frequent is the macrophage migration inhibitory factor. It is expected that this work will contribute to the sub-disciplines of natural products and food informatics.

Extended connectivity interaction features: improving binding affinity prediction through chemical description

MOTIVATION

Machine-learning scoring functions (SFs) have been found to outperform standard SFs for binding affinity prediction of protein-ligand complexes. A plethora of reports focus on the implementation of increasingly complex algorithms, while the chemical description of the system has not been fully exploited.

RESULTS

Herein, we introduce Extended Connectivity Interaction Features (ECIF) to describe protein-ligand complexes and build machine-learning SFs with improved predictions of binding affinity. ECIF are a set of protein-ligand atom-type pair counts that take into account each atom's connectivity to describe it and thus define the pair types. ECIF were used to build different machine-learning models to predict protein-ligand affinities (pKd/pKi). The models were evaluated in terms of 'scoring power' on the Comparative Assessment of Scoring Functions 2016. The best models built on ECIF achieved Pearson correlation coefficients of 0.857 when used on its own, and 0.866 when used in combination with ligand descriptors, demonstrating ECIF descriptive power.

AVAILABILITY AND IMPLEMENTATION

Data and code to reproduce all the results are freely available at https://github.com/DIFACQUIM/ECIF.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Progress on open chemoinformatic tools for expanding and exploring the chemical space

The concept of chemical space is a cornerstone in chemoinformatics, and it has broad conceptual and practical applicability in many areas of chemistry, including drug design and discovery. One of the most considerable impacts is in the study of structure-property relationships where the property can be a biological activity or any other characteristic of interest to a particular chemistry discipline. The chemical space is highly dependent on the molecular representation that is also a cornerstone concept in computational chemistry. Herein, we discuss the recent progress on chemoinformatic tools developed to expand and characterize the chemical space of compound data sets using different types of molecular representations, generate visual representations of such spaces, and explore structure-property relationships in the context of chemical spaces. We emphasize the development of methods and freely available tools focusing on drug discovery applications. We also comment on the general advantages and shortcomings of using freely available and easy-to-use tools and discuss the value of using such open resources for research, education, and scientific dissemination.

Rationality over fashion and hype in drug design

The current hype associated with machine learning and artificial intelligence often confuses scientists and students and may lead to uncritical or inappropriate applications of computational approaches. Even the field of computer-aided drug design (CADD) is not an exception. The situation is ambivalent. On one hand, more scientists are becoming aware of the benefits of learning from available data and are beginning to derive predictive models before designing experiments. However, on the other hand, easy accessibility of in silico tools comes at the risk of using them as "black boxes" without sufficient expert knowledge, leading to widespread misconceptions and problems. For example, results of computations may be taken at face value as "nothing but the truth" and data visualization may be used only to generate "pretty and colorful pictures". Computational experts might come to the rescue and help to re-direct such efforts, for example, by guiding interested novices to conduct meaningful data analysis, make scientifically sound predictions, and communicate the findings in a rigorous manner. However, this is not always ensured. This contribution aims to encourage investigators entering the CADD arena to obtain adequate computational training, communicate or collaborate with experts, and become aware of the fundamentals of computational methods and their given limitations, beyond the hype. By its very nature, this Opinion is partly subjective and we do not attempt to provide a comprehensive guide to the best practices of CADD; instead, we wish to stimulate an open discussion within the scientific community and advocate rational rather than fashion-driven use of computational methods. We take advantage of the open peer-review culture of F1000Research such that reviewers and interested readers may engage in this discussion and obtain credits for their candid personal views and comments. We hope that this open discussion forum will contribute to shaping the future practice of CADD.

Expanding the Chemical Information Science gateway

As chemical information evolves, impacting many chemistry areas, effective ways to disseminate results by the scientific community are also changing. Thus, publication schemes adapt to meet the needs of researchers across disciplines to share high-quality data, information, and knowledge. Since 2015, the F1000Research Chemical Information Science (CIS) gateway has offered an open and unique model to disseminate science at the interface of chemoinformatics, bioinformatics, and several other informatic-related disciplines. In response to the evolution of chemical information science, the F1000Research CIS gateway has incorporated new members to the advisory board. It is also reinforcing and expanding the gateway areas with a particular focus on machine learning and metabolomics. The range of available article types, availability of data, exposure within complementary multidisciplinary F1000Research gateways, and indexing in major bibliographic databases increases the visibility of all contributions. As part of progressing open science in this field, we look forward to your high-quality contributions to the CIS gateway.

Monosubstituted Coumarins Inhibit Epinephrine-Induced Platelet Aggregation Antiplatelet Effect of Monosubstituted Coumarins

Aim

The aim of this study was to evaluate the in vitro effect of coumarin and 15 monosubstituted  derivatives on  the inhibition  of human platelet  aggregation  induced by various  pro-aggregatory agonists, particularly by epinephrine.

Background

The emergence of residual platelet reactivity during the use of conventional antiplatelet agents (acetylsalicylic acid and clopidogrel) is one of the main causes of double therapy´s therapeutic failure. Platelet adrenoceptors participate in residual platelet reactivity. Therefore, it is necessary to develop new antiplatelet agents that inhibit epinephrine-induced platelet aggregation as a new therapeutic strategy. Information on the antiplatelet activity of coumarins in inhibiting epinephrine-induced aggregation is limited.

Objective

The objective of this study was to establish the structure-activity relationship (SAR) of coumarin derivatives with hydroxy, methoxy, and acetoxy groups in different positions of the coumarin nucleus to identify the most active molecules. Moreover, this study aimed to use in silico studies to suggest potential drug targets to which the molecules bind to produce antiplatelet effects.

Methods

The platelet aggregation was performed using a Lumi-aggregometer; the inhibitory activity of 16 compounds were evaluated by inducing the aggregation of human platelets (250 × 10³/μl) with epinephrine (10 µM), collagen (2 µg/ml) or ADP (10 µM). The aggregation of control platelets was considered 100% of the response for each pro-aggregatory agonist.

Results

Eleven molecules inhibited epinephrine-induced aggregation, with 3-acetoxycoumarin and 7-methoxycoumarin being the most active. Only coumarin inhibited collagen-induced platelet aggregation, but no molecule showed activity when using ADP as an inducer.

Conclusions

In silico studies suggest that most active molecules might have antagonistic interactions in the α₂ and β₂ adrenoceptors. The antiplatelet actions of these coumarins have the potential to reduce residual platelet reactivity and thus contribute to the development of future treatments for patients who do not respond adequately to conventional agents.

Latin American databases of natural products: biodiversity and drug discovery against SARS-CoV-2

In this study, we evaluated 3444 Latin American natural products using cheminformatic tools. We also characterized 196 compounds for the first time from the flora of El Salvador that were compared with the databases of secondary metabolites from Brazil, Mexico, and Panama, and 42 969 compounds (natural, semi-synthetic, synthetic) from different regions of the world. The overall analysis was performed using drug-likeness properties, molecular fingerprints of different designs, two parameters similarity, molecular scaffolds, and molecular complexity metrics. It was found that, in general, Salvadoran natural products have a large diversity based on fingerprints. Simultaneously, those belonging to Mexico and Panama present the greatest diversity of scaffolds compared to the other databases. This study provided evidence of the high structural complexity that Latin America's natural products have as a benchmark. The COVID-19 pandemic has had a negative effect on a global level. Thus, in the search for substances that may influence the coronavirus life cycle, the secondary metabolites from El Salvador and Panama were evaluated by docking against the endoribonuclease NSP-15, an enzyme involved in the SARS CoV-2 viral replication. We propose in this study three natural products as potential inhibitors of NSP-15.

Tubulin Inhibitors: A Chemoinformatic Analysis Using Cell-Based Data

Inhibiting the tubulin-microtubules (Tub-Mts) system is a classic and rational approach for treating different types of cancers. A large amount of data on inhibitors in the clinic supports Tub-Mts as a validated target. However, most of the inhibitors reported thus far have been developed around common chemical scaffolds covering a narrow region of the chemical space with limited innovation. This manuscript aims to discuss the first activity landscape and scaffold content analysis of an assembled and curated cell-based database of 851 Tub-Mts inhibitors with reported activity against five cancer cell lines and the Tub-Mts system. The structure-bioactivity relationships of the Tub-Mts system inhibitors were further explored using constellations plots. This recently developed methodology enables the rapid but quantitative assessment of analog series enriched with active compounds. The constellations plots identified promising analog series with high average biological activity that could be the starting points of new and more potent Tub-Mts inhibitors.

Computational Approaches for the Discovery and Development of Pharmacologically Active Natural Products

Natural products continue to be a significant source of active compounds [...].

Epigenetic Target Fishing with Accurate Machine Learning Models

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.

The Acid/Base Characterization of Molecules with Epigenetic Activity

The acidic and basic functional groups in a molecule strongly influence its physicochemical properties, affinity for a macromolecule, pharmacokinetics, and toxicity. For instance, basicity has been correlated with molecular promiscuity, hERG blockade, and phospholipidosis. Nonetheless, no systematic characterization of the acid/base profile of epigenomic databases has been reported. This study describes an analysis of the acidic ionization constant distribution of a library of 7820 compounds with reported activity against epigenetic targets. Furthermore, the epigenomics database's acid/base profile was compared to the reference libraries of food chemicals, natural products, and approved drugs. It was found that the acid/base profile of histone lysine demethylase ligands is more similar to previously approved drugs, and histone acetyltransferase ligands have acidic and basic functional groups largely found in food chemicals and natural products; this support the potential of these libraries for finding new epigenetic inhibitors.

Epigenetic Target Profiler: A Web Server to Predict Epigenetic Targets of Small Molecules

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.

DiaNat-DB: a molecular database of antidiabetic compounds from medicinal plants

Natural products are an invaluable source of molecules with a large variety of biological activities. Interest in natural products in drug discovery is documented in an increasing number of publications of bioactive secondary metabolites. Among those, medicinal plants are one of the most studied for this endeavor. An ever thriving area of opportunity within the field concerns the discovery of antidiabetic natural products. As a result, a vast amount of secondary metabolites are isolated from medicinal plants used against diabetes mellitus but whose information has not been organized systematically yet. Several research articles enumerate antidiabetic compounds, but the lack of a chemical database for antidiabetic metabolites limits their application in drug development. In this work, we present DiaNat-DB, a comprehensive collection of 336 molecules from medicinal plants reported to have in vitro or in vivo antidiabetic activity. We also discuss a chemoinformatic analysis of DiaNat-DB to compare antidiabetic drugs and natural product databases. To further explore the antidiabetic chemical space based on DiaNat compounds, we searched for analogs in ZINC15, an extensive database listing commercially available compounds. This work will help future analyses, design, and development of new antidiabetic drugs. DiaNat-DB and its ZINC15 analogs are freely available at http://rdu.iquimica.unam.mx/handle/20.500.12214/1186.

Medicinal plants are widely used against diabetes mellitus. Here, we provide DiaNat-DB, the first publicly available database of secondary metabolites from medicinal plants, chemically characterized, and integrated with antidiabetic activity.

Informatics for Chemistry, Biology, and Biomedical Sciences

Informatics is growing across disciplines, impacting several areas of chemistry, biology, and biomedical sciences. Besides the well-established bioinformatics discipline, other informatics-based interdisciplinary fields have been evolving over time, such as chemoinformatics and biomedical informatics. Other related research areas such as pharmacoinformatics, food informatics, epi-informatics, materials informatics, and neuroinformatics have emerged more recently and continue to develop as independent subdisciplines. The goals and impacts of each of these disciplines have typically been separately reviewed in the literature. Hence, it remains challenging to identify commonalities and key differences. Herein, we discuss in context three major informatics disciplines in the natural and life sciences including bioinformatics, chemoinformatics, and biomedical informatics and briefly comment on related subdisciplines. We focus the discussion on the definitions, historical background, actual impact, main similarities, and differences and evaluate the dissemination and teaching of bioinformatics, chemoinformatics, and biomedical informatics.

Expanding the Structural Diversity of DNA Methyltransferase Inhibitors

Inhibitors of DNA methyltransferases (DNMTs) are attractive compounds for epigenetic drug discovery. They are also chemical tools to understand the biochemistry of epigenetic processes. Herein, we report five distinct inhibitors of DNMT1 characterized in enzymatic inhibition assays that did not show activity with DNMT3B. It was concluded that the dietary component theaflavin is an inhibitor of DNMT1. Two additional novel inhibitors of DNMT1 are the approved drugs glyburide and panobinostat. The DNMT1 enzymatic inhibitory activity of panobinostat, a known pan inhibitor of histone deacetylases, agrees with experimental reports of its ability to reduce DNMT1 activity in liver cancer cell lines. Molecular docking of the active compounds with DNMT1, and re-scoring with the recently developed extended connectivity interaction features approach, led to an excellent agreement between the experimental IC₅₀ values and docking scores.