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.

In Silico Exploration of the Trypanothione Reductase (TryR) of L. mexicana.

Human leishmaniasis is a neglected tropical disease which affects nearly 1.5 million people every year, with Mexico being an important endemic region. One of the major defense mechanisms of these parasites is based in the polyamine metabolic pathway, as it provides the necessary compounds for its survival. Among the enzymes in this route, trypanothione reductase (TryR), an oxidoreductase enzyme, is crucial for the Leishmania genus' survival against oxidative stress. Thus, it poses as an attractive drug target, yet due to the size and features of its catalytic pocket, modeling techniques such as molecular docking focusing on that region is not convenient. Herein, we present a computational study using several structure-based approaches to assess the druggability of TryR from L. mexicana, the predominant Leishmania species in Mexico, beyond its catalytic site. Using this consensus methodology, three relevant pockets were found, of which the one we call σ-site promises to be the most favorable one. These findings may help the design of new drugs of trypanothione-related diseases.

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 .

Modifications in the piperazine ring of nucleozin affect anti-influenza activity.

The infection caused by the influenza virus is a latent tret. The limited access to vaccines and approved drugs highlights the need for additional antiviral agents. Nucleozin and its analogs have gain attention for their promising anti-influenza activity. To contribute to the advancement of the discovery and design of nucleozin analogs, we analyzed piperazine-modified nucleozin analogs to increase conformational freedom. Also, we describe a new synthetic strategy to obtain nucleozin and its analogues, three molecules were synthesized and two of them were biologically evaluated in vitro. Although the analogues were less active than nucleozin, the loss of activity highlights the need for the piperazine ring to maintain the activity of nucleozin analogs. Interestingly, this result agrees with the prediction of anti-influenza activity made with a QSAR model presented in this work. The proposed model and the synthetic route will be useful for the further development of nucleozin analogs with antiviral activity.

Subtle structural differences of nucleotide analogs may impact SARS-CoV-2 RNA-dependent RNA polymerase and exoribonuclease activity.

The rapid spread and public health impact of the novel SARS-CoV-2 variants that cause COVID-19 continue to produce major global impacts and social distress. Several vaccines were developed in record time to prevent and limit the spread of the infection, thus playing a pivotal role in controlling the pandemic. Although the repurposing of available drugs attempts to provide therapies of immediate access against COVID-19, there is still a need for developing specific treatments for this disease. Remdesivir, molnupiravir and Paxlovid remain the only evidence-supported antiviral drugs to treat COVID-19 patients, and only in severe cases. To contribute on the search of potential Covid-19 therapeutic agents, we targeted the viral RNA-dependent RNA polymerase (RdRp) and the exoribonuclease (ExoN) following two strategies. First, we modeled and analyzed nucleoside analogs sofosbuvir, remdesivir, favipiravir, ribavirin, and molnupiravir at three key binding sites on the RdRp-ExoN complex. Second, we curated and virtually screened a database containing 517 nucleotide analogs in the same binding sites. Finally, we characterized key interactions and pharmacophoric features presumably involved in viral replication halting at multiple sites. Our results highlight structural modifications that might lead to more potent SARS-CoV-2 inhibitors against an expansive range of variants and provide a collection of nucleotide analogs useful for screening campaigns.

Epilogue to the Gerald Maggiora Festschrift: a tribute to an exemplary mentor, colleague, collaborator, and innovator.

In May 2022, JCAMD published a Special Issue in honor of Gerald (Gerry) Maggiora, whose scientific leadership over many decades advanced the fields of computational chemistry and chemoinformatics for drug discovery. Along the way, he has impacted many researchers in both academia and the pharmaceutical industry. In this Epilogue, we explain the origins of the Festschrift and present a series of first-hand vignettes, in approximate chronological sequence, that together paint a picture of this remarkable man. Whether they highlight Gerry's endless curiosity about molecular life sciences or his willingness to challenge conventional wisdom or his generous support of junior colleagues and peers, these colleagues and collaborators are united in their appreciation of his positive influence. These tributes also reflect key trends and themes during the evolution of modern drug discovery, seen through the lens of people who worked with a visionary leader. Junior scientists will find an inspiring roadmap for creative collegiality and collaboration.

Encoding mu-opioid receptor biased agonism with interaction fingerprints.

Opioids are potent painkillers, however, their therapeutic use requires close medical monitoring to diminish the risk of severe adverse effects. The G-protein biased agonists of the µ-opioid receptor (MOR) have shown safer therapeutic profiles than non-biased ligands. In this work, we performed extensive all-atom molecular dynamics simulations of two markedly biased ligands and a balanced reference molecule. From those simulations, we identified a protein-ligand interaction fingerprint that characterizes biased ligands. Then, we built and virtually screened a database containing 68,740 ligands with proven or potential GPCR agonistic activity. Exemplary molecules that fulfill the interacting pattern for biased agonism are showcased, illustrating the usefulness of this work for the search of biased MOR ligands and how this contributes to the understanding of MOR biased signaling.

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.

Predictive Global Models of Cruzain Inhibitors with Large Chemical Coverage.

Chagas disease affects 8-11 million people worldwide, most of them living in Latin America. Moreover, migratory phenomena have spread the infection beyond endemic areas. Efforts for the development of new pharmacological therapies are paramount as the pharmacological profile of the two marketed drugs currently available, nifurtimox and benznidazole, needs to be improved. Cruzain, a parasitic cysteine protease, is one of the most attractive biological targets due to its roles in parasite survival and immune evasion. In this work, we compiled and curated a database of diverse cruzain inhibitors previously reported in the literature. From this data set, quantitative structure-activity relationship (QSAR) models for the prediction of their pIC50 values were generated using k-nearest neighbors and random forest algorithms. Local and global models were calculated and compared. The statistical parameters for internal and external validation indicate a significant predictability, with q loo 2 values around 0.66 and 0.61 and external R 2 coefficients of 0.725 and 0.766. The applicability domain is quantitatively defined, according to QSAR good practices, using the leverage and similarity methods. The models described in this work are readily available in a Python script for the discovery of novel cruzain inhibitors.

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.

Membrane Curvature Revisited-the Archetype of Rhodopsin Studied by Time-Resolved Electronic Spectroscopy.

G-protein-coupled receptors (GPCRs) comprise the largest and most pharmacologically targeted membrane protein family. Here, we used the visual receptor rhodopsin as an archetype for understanding membrane lipid influences on conformational changes involved in GPCR activation. Visual rhodopsin was recombined with lipids varying in their degree of acyl chain unsaturation and polar headgroup size using 1-palmitoyl-2-oleoyl-sn-glycero- and 1,2-dioleoyl-sn-glycerophospholipids with phosphocholine (PC) or phosphoethanolamine (PE) substituents. The receptor activation profile after light excitation was measured using time-resolved ultraviolet-visible spectroscopy. We discovered that more saturated POPC lipids back shifted the equilibrium to the inactive state, whereas the small-headgroup, highly unsaturated DOPE lipids favored the active state. Increasing unsaturation and decreasing headgroup size have similar effects that combine to yield control of rhodopsin activation, and necessitate factors beyond proteolipid solvation energy and bilayer surface electrostatics. Hence, we consider a balance of curvature free energy with hydrophobic matching and demonstrate how our data support a flexible surface model (FSM) for the coupling between proteins and lipids. The FSM is based on the Helfrich formulation of membrane bending energy as we previously first applied to lipid-protein interactions. Membrane elasticity and curvature strain are induced by lateral pressure imbalances between the constituent lipids and drive key physiological processes at the membrane level. Spontaneous negative monolayer curvature toward water is mediated by unsaturated, small-headgroup lipids and couples directly to GPCR activation upon light absorption by rhodopsin. For the first time to our knowledge, we demonstrate this modulation in both the equilibrium and pre-equilibrium evolving states using a time-resolved approach.

DARK Classics in Chemical Neuroscience: Salvinorin A.

Salvinorin A is the main bioactive compound in Salvia divinorum, an endemic plant with ancestral use by the inhabitants of the Mazateca mountain range (Sierra Mazateca) in Oaxaca, México. The main use of la pastora, as locally known, is in spiritual rites due to its extraordinary hallucinogenic effects. Being the first known nonalkaloidal opioid-mediated psychotropic molecule, salvinorin A set new research areas in neuroscience. The absence of a protonated amine group, common to all previously known opioids, results in a fast metabolism with the concomitant fast elimination and swift loss of activity. The worldwide spread and psychotropic effects of salvinorin A account for its misuse and classification as a drug of abuse. Consequently, salvinorin A and Salvia divinorum are now banned in many countries. Several synthetic efforts have been focused on the improvement of physicochemical and biological properties of salvinorin A: from total synthesis to hundreds of analogues. In this Review, we discuss the impact of salvinorin A in chemistry and neuroscience covering the historical relevance, isolation from natural sources, synthetic efforts, and pharmacological and safety profiles. Altogether, the chemistry behind and the taboo that encloses salvinorin A makes it one of the most exquisite naturally occurring drugs.

The impact of chemoinformatics on drug discovery in the pharmaceutical industry.

Introduction: Even though there have been substantial advances in our understanding of biological systems, research in drug discovery is only just now beginning to utilize this type of information. The single-target paradigm, which exemplifies the reductionist approach, remains a mainstay of drug research today. A deeper view of the complexity involved in drug discovery is necessary to advance on this field.Areas covered: This perspective provides a summary of research areas where cheminformatics has played a key role in drug discovery, including of the available resources as well as a personal perspective of the challenges still faced in the field.Expert opinion: Although great strides have been made in the handling and analysis of biological and pharmacological data, more must be done to link the data to biological pathways. This is crucial if one is to understand how drugs modify disease phenotypes, although this will involve a shift from the single drug/single target paradigm that remains a mainstay of drug research. Moreover, such a shift would require an increased awareness of the role of physiology in the mechanism of drug action, which will require the introduction of new mathematical, computer, and biological methods for chemoinformaticians to be trained in.

Structure-based identification of a potential non-catalytic binding site for rational drug design in the fructose 1,6-biphosphate aldolase from Giardia lamblia.

Giardia lamblia is the causal agent of giardiasis, one of the most prevalent parasitosis in the world. Even though effective pharmacotherapies against this parasite are available, the disadvantages associated with its use call for the development of new antigiardial compounds. Based on the Giardia dependence on glycolysis as a main energy source, glycolytic enzymes appear to be attractive targets with antiparasitic potential. Among these, fructose 1,6-biphosphate aldolase (GlFBPA) has been highlighted as a promising target for drug design. Current efforts are based on the design of competitive inhibitors of GlFBPA; however, in the kinetic context of metabolic pathways, competitive inhibitors seem to have low potential as therapeutic agents. In this work, we performed an experimental and in silico structure-based approach to propose a non-catalytic binding site which could be used as a hot spot for antigardial drug design. The druggability of the selected binding site was experimentally tested; the alteration of the selected region by site directed mutagenesis disturbs the catalytic properties and the stability of the enzyme. A computational automated search of binding sites supported the potential of this region as functionally relevant. A preliminary docking study was performed, in order to explore the feasibility and type of molecules to be able to accommodate in the proposed binding region. Altogether, the results validate the proposed region as a specific molecular binding site with pharmacological potential.

Enhancing Acute Oral Toxicity Predictions by using Consensus Modeling and Algebraic Form-Based 0D-to-2D Molecular Encodes.

Quantitative structure-activity relationships (QSAR) are introduced to predict acute oral toxicity (AOT), by using the QuBiLS-MAS (acronym for quadratic, bilinear and N-Linear maps based on graph-theoretic electronic-density matrices and atomic weightings) framework for the molecular encoding. Three training sets were employed to build the models: EPA training set (5931 compounds), EPA-full training set (7413 compounds), and Zhu training set (10 152 compounds). Additionally, the EPA test set (1482 compounds) was used for the validation of the QSAR models built on the EPA training set, while the ProTox (425 compounds) and T3DB (284 compounds) external sets were employed for the assessment of all the models. The k-nearest neighbor, multilayer perceptron, random forest, and support vector machine procedures were employed to build several base (individual) models. The base models with REPA-training ≥ 0.75 ( R = correlation coefficient) and MAEEPA-training ≤ 0.5 (MAE = mean absolute error) were retained to build consensus models. As a result, two consensus models based on the minimum operator and denoted as M19 and M22, as well as a consensus model based on the weighted average operator and denoted as M24, were selected as the best ones for each training set considered. According to the applicability domain (AD) analysis performed, model M19 (built on the EPA training set) has MAEtest-AD = 0.4044, MAEProTox-AD = 0.4067 and MAET3DB-AD = 0.2586 on the EPA test set, ProTox external set, and T3DB external set, respectively; whereas model M22 (built on the EPA-full set) and model M24 (built on the Zhu set) present MAEProTox-AD = 0.3992 and MAET3DB-AD = 0.2286, and MAEProTox-AD = 0.3773 and MAET3DB-AD = 0.2471 on the two external sets accounted for, respectively. These outcomes were compared and statistically validated with respect to 14 QSAR methods (e.g., admetSAR, ProTox-II) from the literature. As a result, model M22 presents the best overall performance. In addition, a retrospective study on 261 withdrawn drugs due to their toxic/side effects was performed, to assess the usefulness of prospectively using the QSAR models proposed in the labeling of chemicals. A comparison with regard to the methods from the literature was also made. As a result, model M22 has the best ability of labeling a compound as toxic according to the globally harmonized system of classification and labeling of chemicals. Therefore, it can be concluded that the models proposed, especially model M22, constitute prominent tools for studying AOT, at providing the best results among all the methods examined. A freely available software was also developed to be used in virtual screening tasks ( http://tomocomd.com/apps/ptoxra ).

Distribution of toxicity values across different species and modes of action of pesticides from PESTIMEP and PPDB databases.

The continuous use of compounds contained in commodities such as processed food, medicines, and pesticides, demands safety measures, in particular, for those in direct contact with humans and the environment. Safety measures have evolved and regulations are now in place around the globe. In the case of pesticides, attempts have been made to use toxicological data to inform of potentially harmful compounds either across species, on different routes of exposure, or entirely new chemicals. The generation of models, based on statistical and molecular modeling studies, allows for such predictions. However, the use of these models is framed by the available data, the experimental errors, the complexity of the measurement, and the available computational algorithms, among other factors. In this work, we present the methodologies used for extrapolation across different species and routes of administration and show the appropriateness of developing predictive models of pesticides based on their type and mode of action. The analyses include comparisons based on structural characteristics and physicochemical properties. Whenever possible, the scope and limitations of the methodologies are discussed. We expect that this work will serve as a useful introductory guide of the tools employed in the toxicity assessment of agrochemical compounds.