MolPredictX: A Pioneer Mobile App Version for Online Biological Activity Predictions by Machine Learning Models.

MolPredictX is a free-access web tool in which it is possible to analyze the prediction of biological activity of chemical molecules. MolPredictX has been available online to the general public for just over a year and has now gone through its first update. We also developed its version for android, being the first free app capable of predicting biological activities. MolPredictX is available for free at https://www.molpredictX.ufpb.br/ , and its mobile application version can be obtained from Google Play.

Computational Drug Design Approaches for the Identification of Novel Antidiabetic Compounds from Natural Resources through Molecular Docking, ADMET, and Toxicological Studies.

Type 2 diabetes mellitus (T2DM) is usually depicted by relative insulin deficiency, raised blood glucose levels, and the predominant risk factor, insulin resistance. Hence, the development of insulin sensitizer drugs targeting PPAR-γ receptors has expanded enormous interest as an attractive choice for T2DM treatment. Thiazolidinediones (TZD) enhance insulin sensitivity either by directly functioning on gene transcription of the PPARγ receptor related to glucose homeostasis or by systemic sensitization of insulin and, therefore, improved hyperglycemia in a wide range of patients. However, severe complications and adverse effects of TZDs necessitate the development of an efficacious and reliable insulin sensitizer from alternative resources. On the contrary, Nature is a rich source of anticipated effective and safer medicine; more than fifty percent of drugs on the market are developed from natural products. Hence, searching for a new PPAR-γ agonist from bioactive secondary compounds of medicinal plants along with greater efficacy and safety is a recognized and consistent tactic for developing novel antidiabetic agents. Pulicaria jaubertii is a fragrant perennial aromatic plant with anti-inflammatory, antidiabetic, antimicrobial, antimalarial, and insecticidal properties. The current study was designed to use a computer-aided drug design to explore the best antidiabetic compounds from P. jaubertii. Herein, the molecular docking study of 80 investigated ligands against the PPAR-γ receptor identifies the highest docking score for five ligands ranging from -8.9 kcal/mol to 8.0 kcal/mol, which is also more significant than the standard drug pioglitazone (-7.7 kcal/mol) determined by the PyRx 8.0 virtual screening software. GLN286, CYS285, SER289, TYR473, MET364, ARG288, ILE341, and LEU333 residues are found to be significant contributors to the non-bonded interaction between ligands and receptors. Molecular electrostatic potential (MEP), DFT, molecular orbital (MO), ADMET, and toxicological analyses were performed on the selected five high-scored ligands of P. jaubertii. Results documented that all investigated ligands, especially L4, show considerably excellent profiles in molecular docking, MEP, DFT, MO, ADMET, and toxicological predictions, suggesting our drug-designing approaches may contribute to the development of a novel antidiabetic drug for the treatment of T2DM from natural resources.

Wee1 inhibitor optimization through deep-learning-driven decision making.

Deep learning has gained increasing attention in recent years, yielding promising results in hit screening and molecular optimization. Herein, we employed an efficient strategy based on multiple deep learning techniques to optimize Wee1 inhibitors, which involves activity interpretation, scaffold-based molecular generation, and activity prediction. Starting from our in-house Wee1 inhibitor GLX0198 (IC50 = 157.9 nM), we obtained three optimized compounds (IC50 = 13.5 nM, 33.7 nM, and 47.1 nM) out of five picked molecules. Further minor modifications on these compounds led to the identification of potent Wee1 inhibitors with desirable inhibitory effects on multiple cancer cell lines. Notably, the best compound 13 exhibited superior cancer cell inhibition, with IC50 values below 100 nM in all tested cancer cells. These results suggest that deep learning can greatly facilitate decision-making at the stage of molecular optimization.

Structure-Aided Computational Design of Triazole-Based Targeted Covalent Inhibitors of Cruzipain.

Cruzipain (CZP), the major cysteine protease present in T. cruzi, the ethiological agent of Chagas disease, has attracted particular attention as a therapeutic target for the development of targeted covalent inhibitors (TCI). The vast chemical space associated with the enormous molecular diversity feasible to explore by means of modern synthetic approaches allows the design of CZP inhibitors capable of exhibiting not only an efficient enzyme inhibition but also an adequate translation to anti-T. cruzi activity. In this work, a computer-aided design strategy was developed to combinatorially construct and screen large libraries of 1,4-disubstituted 1,2,3-triazole analogues, further identifying a selected set of candidates for advancement towards synthetic and biological activity evaluation stages. In this way, a virtual molecular library comprising more than 75 thousand diverse and synthetically feasible analogues was studied by means of molecular docking and molecular dynamic simulations in the search of potential TCI of CZP, guiding the synthetic efforts towards a subset of 48 candidates. These were synthesized by applying a Cu(I)-catalyzed azide-alkyne cycloaddition (CuAAC) centered synthetic scheme, resulting in moderate to good yields and leading to the identification of 12 hits selectively inhibiting CZP activity with IC50 in the low micromolar range. Furthermore, four triazole derivatives showed good anti-T. cruzi inhibition when studied at 50 µM; and Ald-6 excelled for its high antitrypanocidal activity and low cytotoxicity, exhibiting complete in vitro biological activity translation from CZP to T. cruzi. Overall, not only Ald-6 merits further advancement to preclinical in vivo studies, but these findings also shed light on a valuable chemical space where molecular diversity might be explored in the search for efficient triazole-based antichagasic agents.

Computer-Aided Drug Design Using the Fragment Molecular Orbital Method: Current Status and Future Applications for SBDD.

Owing to the increasing use of computers, computer-aided drug design (CADD) has become an essential component of drug discovery research. In structure-based drug design (SBDD), including inhibitor design and in silico screening of drug target molecules, concordance with wet experimental data is important to provide insights on unique perspectives derived from calculations. Fragment molecular orbital (FMO) method is a quantum chemical method that facilitates precise energy calculations. Fragmentation method makes it possible to apply the quantum chemical method to biological macromolecules for energy calculation based on the electron behavior. Furthermore, interaction energies calculated on a residue-by-residue basis via fragmentation aid in the analysis of interactions between the target and ligand molecule residues and molecular design. In this review, we outline the recent developments in SBDD and FMO methods and highlight the prospects of developing machine learning approaches for large computational data using the FMO method.

From Nature to Drug: Overview and CADD Approach of Anacardic Acid to Propose their Biological Potential.

Anacardic acids are natural compounds found in various plant families, such as Anacardiaceae, Geraniaceae, Ginkgoaceae, and Myristicaceae, among others. Several activities have been reported regarding these compounds, including antibacterial, antioxidant, anticancer, anti-inflammatory, and antiviral activities, showing the potential therapeutic applicability of these compounds. From a chemical point of view, they are structurally made up of salicylic acids substituted by an alkyl chain containing unsaturated bonds, which can vary in number and position, determining their bioactivity and differentiating them from the various existing forms. Our work aimed to explore the potential of anacardic acids, based on studies that address the bioactivity of these compounds, as well as to establish a greater understanding of the structure-activity relationship of these compounds through in silico methods, with a focus on the elucidation of a possible drug target through the application of computer-aided drug design, CADD.

Teaching old docks new tricks with machine learning enhanced ensemble docking.

We here introduce Ensemble Optimizer (EnOpt), a machine-learning tool to improve the accuracy and interpretability of ensemble virtual screening (VS). Ensemble VS is an established method for predicting protein/small-molecule (ligand) binding. Unlike traditional VS, which focuses on a single protein conformation, ensemble VS better accounts for protein flexibility by predicting binding to multiple protein conformations. Each compound is thus associated with a spectrum of scores (one score per protein conformation) rather than a single score. To effectively rank and prioritize the molecules for further evaluation (including experimental testing), researchers must select which protein conformations to consider and how best to map each compound's spectrum of scores to a single value, decisions that are system-specific. EnOpt uses machine learning to address these challenges. We perform benchmark VS to show that for many systems, EnOpt ranking distinguishes active compounds from inactive or decoy molecules more effectively than traditional ensemble VS methods. To encourage broad adoption, we release EnOpt free of charge under the terms of the MIT license.

Protein language models are performant in structure-free virtual screening.

Hitherto virtual screening (VS) has been typically performed using a structure-based drug design paradigm. Such methods typically require the use of molecular docking on high-resolution three-dimensional structures of a target protein-a computationally-intensive and time-consuming exercise. This work demonstrates that by employing protein language models and molecular graphs as inputs to a novel graph-to-transformer cross-attention mechanism, a screening power comparable to state-of-the-art structure-based models can be achieved. The implications thereof include highly expedited VS due to the greatly reduced compute required to run this model, and the ability to perform early stages of computer-aided drug design in the complete absence of 3D protein structures.

Advances in structure-based drug design targeting membrane protein markers in prostate cancer.

Prostate cancer (PCa) is one of the leading cancers in men and the lack of suitable biomarkers or their modulators results in poor prognosis. Membrane proteins (MPs) have a crucial role in the development and progression of PCa and can be attractive therapeutic targets. However, experimental limitations in targeting MPs hinder effective biomarker and inhibitor discovery. To overcome this barrier, computational methods can yield structural insights and screen large libraries of compounds, accelerating lead identification and optimization. In this review, we examine current breakthroughs in computer-aided drug design (CADD), with emphasis on structure-based approaches targeting the most relevant membrane-bound PCa biomarkers.

Advances in Developing Small Molecule Drugs for Alzheimer's Disease.

Alzheimer's disease (AD) is the most common type of dementia among middle-aged and elderly individuals. Accelerating the prevention and treatment of AD has become an urgent problem. New technology including Computer-aided drug design (CADD) can effectively reduce the medication cost for patients with AD, reduce the cost of living, and improve the quality of life of patients, providing new ideas for treating AD. This paper reviews the pathogenesis of AD, the latest developments in CADD and other small-molecule docking technologies for drug discovery and development; the current research status of small-molecule compounds for AD at home and abroad from the perspective of drug action targets; and the development trend of new drug development for AD in the future.

Hamiltonian diversity: effectively measuring molecular diversity by shortest Hamiltonian circuits.

In recent years, significant advancements have been made in molecular generation algorithms aimed at facilitating drug development, and molecular diversity holds paramount importance within the realm of molecular generation. Nonetheless, the effective quantification of molecular diversity remains an elusive challenge, as extant metrics exemplified by Richness and Internal Diversity fall short in concurrently encapsulating the two main aspects of such diversity: quantity and dissimilarity. To address this quandary, we propose Hamiltonian diversity, a novel molecular diversity metric predicated upon the shortest Hamiltonian circuit. This metric embodies both aspects of molecular diversity in principle, and we implement its calculation with high efficiency and accuracy. Furthermore, through empirical experiments we demonstrate the high consistency of Hamiltonian diversity with real-world chemical diversity, and substantiate its effects in promoting diversity of molecular generation algorithms. Our implementation of Hamiltonian diversity in Python is available at: https://github.com/HXYfighter/HamDiv .Scientific contributionWe propose a more rational molecular diversity metric for the community of cheminformatics and drug development. This metric can be applied to evaluation of existing molecular generation methods and enhancing drug design algorithms.

Synthesis and characterization of gold(I) thiolate derivatives and bimetallic complexes for HIV inhibition.

Introduction: The human immunodeficiency virus (HIV) remains a significant global health concern, with a reported high infection rate of 38.4 million cases globally; an estimated 2 million new infections and approximately 700,000 HIV/AIDS-related deaths were reported in 2021. Despite the advent of anti-retroviral therapy (ART), HIV/AIDS persists as a chronic disease. To combat this, several studies focus on developing inhibitors targeting various stages of the HIV infection cycle, including HIV-1 protease. This study aims to synthesize and characterize novel glyco diphenylphosphino metal complexes with potential HIV inhibitory properties. Method: A series of new gold(I) thiolate derivatives and three bimetallic complexes, incorporating amino phosphines and thiocarbohydrate as auxiliary ligands, were synthesized using procedures described by Jiang, et al. (2009) and Coetzee et al. (2007). Structural elucidation and purity assessment of the synthesized compounds (1-11) were conducted using micro-analysis, NMR, and infrared spectrometry. Results and Discussion: Using molecular modeling techniques, three of the metal complexes were identified as potential HIV protease inhibitors, exhibiting strong binding affinity interactions with binding pocket residues. These inhibitors demonstrated an ability to inhibit the flexibility of the flap regions of the HIV protease, similar to the known HIV protease inhibitor, darunavir. This study sheds light on the promising avenues for the development of novel therapeutic agents against HIV/AIDS.

The Role of Water Networks in Phosphodiesterase Inhibitor Dissociation and Kinetic Selectivity.

In search of new opportunities to develop Trypanosoma brucei phosphodiesterase B1 (TbrPDEB1) inhibitors that have selectivity over the off-target human PDE4 (hPDE4), different stages of a fragment-growing campaign were studied using a variety of biochemical, structural, thermodynamic, and kinetic binding assays. Remarkable differences in binding kinetics were identified and this kinetic selectivity was explored with computational methods, including molecular dynamics and interaction fingerprint analyses. These studies indicate that a key hydrogen bond between GlnQ.50 and the inhibitors is exposed to a water channel in TbrPDEB1, leading to fast unbinding. This water channel is not present in hPDE4, leading to inhibitors with a longer residence time. The computer-aided drug design protocols were applied to a recently disclosed TbrPDEB1 inhibitor with a different scaffold and our results confirm that shielding this key hydrogen bond through disruption of the water channel represents a viable design strategy to develop more selective inhibitors of TbrPDEB1. Our work shows how computational protocols can be used to understand the contribution of solvent dynamics to inhibitor binding, and our results can be applied in the design of selective inhibitors for homologous PDEs found in related parasites.

Cheminformatics-based analysis identified (Z)-2-(2,5-dimethoxy benzylidene)-6-(2-(4-methoxyphenyl)-2-oxoethoxy) benzofuran-3(2H)-one as an inhibitor of Marburg replication by interacting with NP.

The highly pathogenic Marburg virus (MARV) is a member of the Filoviridae family, a non-segmented negative-strand RNA virus. This article represents the computer-aided drug design (CADD) approach for identifying drug-like compounds that prevent the MARV virus disease by inhibiting nucleoprotein, which is responsible for their replication. This study used a wide range of in silico drug design techniques to identify potential drugs. Out of 368 natural compounds, 202 compounds passed ADMET, and molecular docking identified the top two molecules (CID: 1804018 and 5280520) with a high binding affinity of -6.77 and -6.672 kcal/mol, respectively. Both compounds showed interactions with the common amino acid residues SER_216, ARG_215, TYR_135, CYS_195, and ILE_108, which indicates that lead compounds and control ligands interact in the common active site/catalytic site of the protein. The negative binding free energies of CID: 1804018 and 5280520 were -66.01 and -31.29 kcal/mol, respectively. Two lead compounds were re-evaluated using MD modeling techniques, which confirmed CID: 1804018 as the most stable when complexed with the target protein. PC3 of the (Z)-2-(2,5-dimethoxybenzylidene)-6-(2-(4-methoxyphenyl)-2-oxoethoxy) benzofuran-3(2H)-one (CID: 1804018) was 8.74 %, whereas PC3 of the 2'-Hydroxydaidzein (CID: 5280520) was 11.25 %. In this study, (Z)-2-(2,5-dimethoxybenzylidene)-6-(2-(4-methoxyphenyl)-2-oxoethoxy) benzofuran-3(2H)-one (CID: 1804018) unveiled the significant stability of the proteins' binding site in ADMET, Molecular docking, MM-GBSA and MD simulation analysis studies, which also showed a high negative binding free energy value, confirming as the best drug candidate which is found in Angelica archangelica which may potentially inhibit the replication of MARV nucleoprotein.

Unveiling the Potential of Bergenia Phenolics: Vitexin's Role in Allosteric Modulation of PBP2a as a Strategy against MRSA Resistance.

BACKGROUND: For cell wall biosynthesis, drug-resistant S. aureus uses a special protein called PBP2a, even when antibiotics are present and stop its natural processes from working. To combat this, novel therapies are required to specifically target PBP2a with greater efficacy. METHODS: Using computational approaches, we screened nine phenolic compounds from other Bergenia species, including Bergenia ciliata, Begenia ligulata, Bergenia purpurascens, and Ber-genia stracheyi, against the PBP2a allosteric site to explore the potential interaction between phe-nolic compounds and a specific region of PBP2a known as the allosteric site. RESULTS: Based on interaction patterns and estimated affinity, vitexin has been found to be the most prominent phenolic compound. We performed MD simulations on vitexin and ceftazidime as control molecules based on the docking results. The binding free energy estimates of vitexin (-94.48 +/- 17.92 kJ/mol) using MM/PBSA were lower than those of the control (-67.61 +/- 12.29 kJ/mol), which suggests that vitexin may be able to inhibit PBP2a activity in MRSA. CONCLUSION: It has been intriguing to observe a correlation between the affinity of the lead com-pounds at the allosteric site and the modification of Tyr446, the active site gatekeeper residue in PBP2a. Our findings have implied that lead compounds can either directly or indirectly decrease PBP2a activity by inducing allosteric site change in conventional medicine.

Design, in silico Evaluation, and Determination of Antitumor Activity of Potential Inhibitors Against Protein Kinases: Application to BCR-ABL Tyrosine Kinase.

Despite significant progress made over the past two decades in the treatment of chronic myeloid leukemia (CML), there is still an unmet need for effective and safe agents to treat patients with resistance and intolerance to the drugs used in clinic. In this work, we designed 2-arylaminopyrimidine amides of isoxazole-3-carboxylic acid, assessed in silico their inhibitory potential against Bcr-Abl tyrosine kinase, and determined their antitumor activity in K562 (CML), HL-60 (acute promyelocytic leukemia), and HeLa (cervical cancer) cells. Based on the analysis of computational and experimental data, three compounds with the antitumor activity against K562 and HL-60 cells were identified. The lead compound efficiently suppressed the growth of these cells, as evidenced by the low IC50 values of 2.8 ± 0.8 µM (K562) and 3.5 ± 0.2 µM (HL-60). The obtained compounds represent promising basic structures for the design of novel, effective, and safe anticancer drugs able to inhibit the catalytic activity of Bcr-Abl kinase by blocking the ATP-binding site of the enzyme.

Using Multiscale Molecular Modeling to Analyze Possible NS2b-NS3 Protease Inhibitors from Philippine Medicinal Plants.

Within the field of Philippine folkloric medicine, the utilization of indigenous plants like Euphorbia hirta (tawa-tawa), Carica papaya (papaya), and Psidium guajava (guava) as potential dengue remedies has gained attention. Yet, limited research exists on their comprehensive effects, particularly their anti-dengue activity. This study screened 2944 phytochemicals from various Philippine plants for anti-dengue activity. Absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiling provided 1265 compounds demonstrating pharmacokinetic profiles suitable for human use. Molecular docking targeting the dengue virus NS2b-NS3 protease's catalytic triad (Asp 75, Ser 135, and His 51) identified ten ligands with higher docking scores than reference compounds idelalisib and nintedanib. Molecular dynamics simulations confirmed the stability of eight of these ligand-protease complexes. Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) analysis highlighted six ligands, including veramiline (-80.682 kJ/mol), cyclobranol (-70.943 kJ/mol), chlorogenin (-63.279 kJ/mol), 25beta-Hydroxyverazine (-61.951 kJ/mol), etiolin (-59.923 kJ/mol), and ecliptalbine (-56.932 kJ/mol) with favorable binding energies, high oral bioavailability, and drug-like properties. This integration of traditional medical knowledge with advanced computational drug discovery methods paves new pathways for the development of treatments for dengue.

Structure-Based Identification of Novel Histone Deacetylase 4 (HDAC4) Inhibitors.

Histone deacetylases (HDACs) are important cancer drug targets. Existing FDA-approved drugs target the catalytic pocket of HDACs, which is conserved across subfamilies (classes) of HDAC. However, engineering specificity is an important goal. Herein, we use molecular modeling approaches to identify and target potential novel pockets specific to Class IIA HDAC-HDAC4 at the interface between HDAC4 and the transcriptional corepressor component protein NCoR. These pockets were screened using an ensemble docking approach combined with consensus scoring to identify compounds with a different binding mechanism than the currently known HDAC modulators. Binding was compared in experimental assays between HDAC4 and HDAC3, which belong to a different family of HDACs. HDAC4 was significantly inhibited by compound 88402 but not HDAC3. Two other compounds (67436 and 134199) had IC50 values in the low micromolar range for both HDACs, which is comparable to the known inhibitor of HDAC4, SAHA (Vorinostat). However, both of these compounds were significantly weaker inhibitors of HDAC3 than SAHA and thus more selective, albeit to a limited extent. Five compounds exhibited activity on human breast carcinoma and/or urothelial carcinoma cell lines. The present result suggests potential mechanistic and chemical approaches for developing selective HDAC4 modulators.

Insights into the Effects of Ligand Binding on Leucyl-tRNA Synthetase Inhibitors for Tuberculosis: In Silico Analysis and Isothermal Titration Calorimetry Validation.

Incidences of drug-resistant tuberculosis have become common and are rising at an alarming rate. Aminoacyl t-RNA synthetase has been validated as a newer target against Mycobacterium tuberculosis. Leucyl t-RNA synthetase (LeuRS) is ubiquitously found in all organisms and regulates transcription, protein synthesis, mitochondrial RNA cleavage, and proofreading of matured t-RNA. Leucyl t-RNA synthetase promotes growth and development and is the key enzyme needed for biofilm formation in Mycobacterium. Inhibition of this enzyme could restrict the growth and development of the mycobacterial population. A database consisting of 2734 drug-like molecules was screened against leucyl t-RNA synthetase enzymes through virtual screening. Based on the docking scores and MMGBSA energy values, the top three compounds were selected for molecular dynamics simulation. The druggable nature of the top three hits was confirmed by predicting their pharmacokinetic parameters. The top three hits-compounds 1035 (ZINC000001543916), 1054 (ZINC000001554197), and 2077 (ZINC000008214483)-were evaluated for their binding affinity toward leucyl t-RNA synthetase by an isothermal titration calorimetry study. The inhibitory activity of these compounds was tested against antimycobacterial activity, biofilm formation, and LeuRS gene expression potential. Compound 1054 (Macimorelin) was found to be the most potent molecule, with better antimycobacterial activity, enzyme binding affinity, and significant inhibition of biofilm formation, as well as inhibition of the LeuRS gene expression. Compound 1054, the top hit compound, has the potential to be used as a lead to develop successful leucyl t-RNA synthetase inhibitors.