Computer-Aided Drug Design: An Overview

Combatting Antibiotic-Resistant Staphylococcus aureus: Discovery of TST1N-224, a Potent Inhibitor Targeting Response Regulator VraRC, through Pharmacophore-Based Screening and Molecular Characterizations

Staphylococcus aureus (S. aureus) is a major global health concern, causing various infections and presenting challenges due to antibiotic resistance. In particular, methicillin-resistant S. aureus, vancomycin-intermediate S. aureus (VISA), and vancomycin-resistant S. aureus pose significant obstacles in treating S. aureus infections. Therefore, the critical need for novel drugs to counter these resistant forms is pressing. Two-component systems (TCSs), integral to bacterial regulation, offer promising targets for disruption. In this study, a comprehensive approach, involving pharmacophore-based inhibitor screening, along with biochemical and biophysical analyses were conducted to identify, characterize, and validate potential inhibitors targeting the response regulator VraRC of S. aureus. The constructed pharmacophore model, Phar-VRPR-N3, demonstrated effectiveness in identifying a potent inhibitor, TST1N-224 (IC50 = 60.2 ± 4.0 μM), against the formation of the VraRC-DNA complex. Notably, TST1N-224 exhibited strong binding to VraRC (KD = 23.4 ± 1.2 μM) using a fast-on-fast-off binding mechanism. Additionally, NMR-based molecular modeling revealed that TST1N-224 predominantly interacts with the α9- and α10-helixes of the DNA-binding domain of VraR, where the interactive and functionally essential residues (N165, K180, S184, and R195) act as hotspots for structure-based inhibitor optimization. Furthermore, TST1N-224 evidently enhanced the susceptibility of VISA to both vancomycin and methicillin. Importantly, TST1N-224 distinguished by 1,2,5,6-tetrathiocane with the 3 and 8 positions modified with ethanesulfonates holds significant potential as a lead compound for the development of new antimicrobial agents.

Metabolites profiling and cheminformatics bioprospection of selected medicinal plants against the main protease and RNA-dependent RNA polymerase of SARS-CoV-2

Despite the existence of some vaccines, SARS-CoV-2 (S-2) infections persist for various reasons relating to vaccine reluctance, rapid mutation rate, and an absence of specific treatments targeted to the infection. Due to their availability, low cost and low toxicity, research into potentially repurposing phytometabolites as therapeutic alternatives has gained attention. Therefore, this study explored the antiviral potential of metabolites of some medicinal plants [Spondias mombin, Macaranga barteri and Dicerocaryum eriocarpum (Sesame plant)] identified using liquid chromatography-mass spectrometry (LCMS) as possible inhibitory agents against the S-2 main protease (S-2 MP) and RNA-dependent RNA polymerase (RP) using computational approaches. Molecular docking was used to identify the compounds with the best affinities for the selected therapeutics targets. Afterwards, compounds with poor physicochemical characteristics, pharmacokinetics, and drug-likeness were screened out. The top-ranked compounds were further subjected to a 120-ns molecular dynamics (MD) simulation. Only quercetin 3-O-rhamnoside (-48.77 kcal/mol) had higher binding free energy than the reference standard (zafirlukast) (-44.99 kcal/mol) against S-2 MP. Conversely, all the top-ranked compounds (ellagic acid hexoside, spiraeoside, apigenin-4'-glucoside and chrysoeriol 7-glucuronide) except gnetin L (-24.24 kcal/mol) had higher binding free energy (-55.19 kcal/mol, -52.75 kcal/mol, -47.22 kcal/mol and -43.35 kcal/mol) respectively, against S-2 RP relative to the reference standard (-34.79 kcal/mol). The MD simulations study further revealed that the investigated inhibitors are thermodynamically stable and form structurally compatible complexes that impede the regular operation of the respective S-2 therapeutic targets. Although, these S-2 therapeutic candidates are promising, further in vitro and in vivo evaluation is required and highly recommended.Communicated by Ramaswamy H. Sarma.

MIFAM-DTI: a drug-target interactions predicting model based on multi-source information fusion and attention mechanism

Accurate identification of potential drug-target pairs is a crucial step in drug development and drug repositioning, which is characterized by the ability of the drug to bind to and modulate the activity of the target molecule, resulting in the desired therapeutic effect. As machine learning and deep learning technologies advance, an increasing number of models are being engaged for the prediction of drug-target interactions. However, there is still a great challenge to improve the accuracy and efficiency of predicting. In this study, we proposed a deep learning method called Multi-source Information Fusion and Attention Mechanism for Drug-Target Interaction (MIFAM-DTI) to predict drug-target interactions. Firstly, the physicochemical property feature vector and the Molecular ACCess System molecular fingerprint feature vector of a drug were extracted based on its SMILES sequence. The dipeptide composition feature vector and the Evolutionary Scale Modeling -1b feature vector of a target were constructed based on its amino acid sequence information. Secondly, the PCA method was employed to reduce the dimensionality of the four feature vectors, and the adjacency matrices were constructed by calculating the cosine similarity. Thirdly, the two feature vectors of each drug were concatenated and the two adjacency matrices were subjected to a logical OR operation. And then they were fed into a model composed of graph attention network and multi-head self-attention to obtain the final drug feature vectors. With the same method, the final target feature vectors were obtained. Finally, these final feature vectors were concatenated, which served as the input to a fully connected layer, resulting in the prediction output. MIFAM-DTI not only integrated multi-source information to capture the drug and target features more comprehensively, but also utilized the graph attention network and multi-head self-attention to autonomously learn attention weights and more comprehensively capture information in sequence data. Experimental results demonstrated that MIFAM-DTI outperformed state-of-the-art methods in terms of AUC and AUPR. Case study results of coenzymes involved in cellular energy metabolism also demonstrated the effectiveness and practicality of MIFAM-DTI. The source code and experimental data for MIFAM-DTI are available at https://github.com/Search-AB/MIFAM-DTI.

Curation and cheminformatics analysis of a Ugi-reaction derived library (URDL) of synthetically tractable small molecules for virtual screening application

Virtual screening (VS) is an important approach in drug discovery and relies on the availability of a virtual library of synthetically tractable molecules. Ugi reaction (UR) represents an important multi-component reaction (MCR) that reliably produces a peptidomimetic scaffold. Recent literature shows that a tactically assembled Ugi adduct can be subjected to further chemical modifications to yield a variety of rings and scaffolds, thus, renewing the interest in this old reaction. Given the reliability and efficiency of UR, we collated an UR derived library (URDL) of small molecules (total = 5773) for VS. The synthesis of the majority of URDL molecules may be carried out in 1-2 pots in a time and cost-effective manner. The detailed analysis of the average property and chemical space of URDL was also carried out using the open-source Datawarrior program. The comparison with FDA-approved oral drugs and inhibitors of protein-protein interactions (iPPIs) suggests URDL molecules are 'clean', drug-like, and conform to a structurally distinct space from the other two categories. The average physicochemical properties of compounds in the URDL library lie closer to iPPI molecules than oral drugs thus suggesting that the URDL resource can be applied to discover novel iPPI molecules. The URDL molecules consist of diverse ring systems, many of which have not been exploited yet for drug design. Thus, URDL represents a small virtual library of drug-like molecules with unexplored chemical space designed for VS. The structures of all molecules of URDL, oral drugs, and iPPI compounds are being made freely accessible as supplementary information for broader application.

Identification of potential death-associated protein kinase-1 (DAPK1) inhibitors by an integrated ligand-based and structure-based computational drug design approach

Death-associated protein kinase 1 (DAPK1) is a calcium/calmodulin (Ca2+/CaM)-dependent serine/threonine kinase that is abundantly expressed in the memory- and cognition-related brain areas. DAPK1 is associated with several pathological hallmarks of Alzheimer's disease (AD); it is an attractive target for designing a novel DAPK1 inhibitor as an effective therapeutic treatment for AD. In the present study, we have used an integrated ligand-based and structure-based drug design method to identify DAPK1 inhibitors. The pharmacophoric features of compound 38 G (PDB ID 4TXC) were mapped, and the models were evaluated using enrichment factor (EF) and goodness of hit (GH) score. The selected models were used to screen Zinc 15 compounds library. The identified hits were passed through drug-likeliness and PAINS filtering. The docking study was performed in three steps to yield molecules with good binding energy and ligand-target interactions. Finally, three hits were obtained, that is, ZINC000020648330, ZINC000006755051 and ZINC000020650468, which were subjected to rigorous molecular dynamics simulation. All three hits exhibited optimal stability under simulated conditions and low predicted toxicity.Communicated by Ramaswamy H. Sarma.

Computational Exploration of Licorice for Lead Compounds against Plasmodium vivax Duffy Binding Protein Utilizing Molecular Docking and Molecular Dynamic Simulation

Plasmodium vivax (P. vivax) is one of the human's most common malaria parasites. P. vivax is exceedingly difficult to control and eliminate due to the existence of extravascular reservoirs and recurring infections from latent liver stages. Traditionally, licorice compounds have been widely investigated against viral and infectious diseases and exhibit some promising results to combat these diseases. In the present study, computational approaches are utilized to study the effect of licorice compounds against P. vivax Duffy binding protein (DBP) to inhibit the malarial invasion to human red blood cells (RBCs). The main focus is to block the DBP binding site to Duffy antigen receptor chemokines (DARC) of RBC to restrict the formation of the DBP-DARC complex. A molecular docking study was performed to analyze the interaction of licorice compounds with the DARC binding site of DBP. Furthermore, the triplicates of molecular dynamic simulation studies for 100 ns were carried out to study the stability of representative docked complexes. The leading compounds such as licochalcone A, echinatin, and licochalcone B manifest competitive results against DBP. The blockage of the active region of DBP resulting from these compounds was maintained throughout the triplicates of 100 ns molecular dynamic (MD) simulation, maintaining stable hydrogen bond formation with the active site residues of DBP. Therefore, the present study suggests that licorice compounds might be good candidates for novel agents against DBP-mediated RBC invasion of P. vivax.

Identification of potential inhibitors of thymidylate synthase (TS) (PDB ID: 6QXH) and nuclear factor kappa-B (NF–κB) (PDB ID: 1A3Q) from Capsicum annuum (bell pepper) towards the development of new therapeutic drugs against colorectal cancer (CRC)

Colorectal cancer is the third most deadly cancer globally. Drug resistance and attendant side effects make the available standard anti-colorectal cancer drugs against target receptors inefficient. Phytochemicals from medicinal plants are safer, cheaper, effective, and heal diseases from the cellular level. This study is aimed at identifying potential inhibitors of thymidylate synthase (TS) and nuclear factor kappa-B (NF–κB) target receptors from Capsicum annuum towards the development of new therapeutic drugs against colorectal cancer via in silico approach. One hundred and fifty (150) ligands previously reported from Capsicum annuum were downloaded from the PubChem database and were subjected to chemo-informatics analyses such as ADMET, drug-likeness, oral bioavailability, bioactivity, and PASS prediction to ascertain their therapeutic and safety profile before docking. The ligands that passed the analyses were docked against TS and NF–κB in duplicate using a creditable docking tool (PyRx). Raltitrexed and emetine were used as the standard drug inhibitors for TS and NF–κB, respectively. The results obtained from this study showed that feruloyl-beta-D-glucose (8.45 kcal/mol), 5-O-caffeoylquinic acid (−8.40 kcal/mol), 5-O-caffeoylquinic acid methyl ester (−7.89 kcal/mol), feruloyl hexoside (−7.40 kcal/mol), O-glucopyranoside (−7.55 kcal/mol), and quercetin (−7.00 kcal/mol) shared the same binding pocket with TS while feruloyl-beta-D-glucose (−7.00 kcal/mol), chlorogenic acid (−6.90 kcal/mol), 5-O-caffeoylquinic acid (−6.90 kcal/mol) and feruloyl hexoside (−6.50 kcal/mol) shared the same pocket with NF–κB. These compounds were selected as best hits due to their excellent inhibitory efficiency and chemoinformatic profiles. Thus, the compounds may function as prospective lead compounds for developing a new anti-colorectal cancer drug.

An alignment-independent three-dimensional quantitative structure-activity relationship study on ron receptor tyrosine kinase inhibitors

Recepteur d'Origine Nantais known as RON is a member of the receptor tyrosine kinase (RTK) superfamily which has recently gained increasing attention as cancer target for therapeutic intervention. The aim of this work was to perform an alignment-independent three-dimensional quantitative structure-activity relationship (3D QSAR) study for a series of RON inhibitors. A 3D QSAR model based on GRid-INdependent Descriptors (GRIND) methodology was generated using a set of 19 compounds with RON inhibitory activities. The generated 3D QSAR model revealed the main structural features important in the potency of RON inhibitors. The results obtained from the presented study can be used in lead optimization projects for designing of novel compounds where inhibition of RON is needed.

Deep generative models for ligand-based de novo design applied to multi-parametric optimization

Multi-parameter optimization (MPO) is a major challenge in new chemical entity (NCE) drug discovery. Recently, promising results were reported for deep learning generative models applied to de novo molecular design, but, to our knowledge, until now no report was made of the value of this new technology for addressing MPO in an actual drug discovery project. In this study, we demonstrate the benefit of applying AI technology in a real drug discovery project. We evaluate the potential of a ligand-based de novo design technology using deep learning generative models to accelerate the obtention of lead compounds meeting 11 different biological activity objectives simultaneously. Using the initial dataset of the project, we built QSAR models for all the 11 objectives, with moderate to high performance (precision between 0.67 and 1.0 on an independent test set). Our DL-based AI de novo design algorithm, combined with the QSAR models, generated 150 virtual compounds predicted as active on all objectives. Eleven were synthetized and tested. The AI-designed compounds met 9.5 objectives on average (i.e., 86% success rate) versus 6.4 (i.e., 58% success rate) for the initial molecules measured on all objectives. One of the AI-designed molecules was active on all 11 measured objectives, and two were active on 10 objectives while being in the error margin of the assay for the last one. The AI algorithm designed compounds with functional groups, which, although being rare or absent in the initial dataset, turned out to be highly beneficial for the MPO.

In Silico Evaluation of Iranian Medicinal Plant Phytoconstituents as Inhibitors against Main Protease and the Receptor-Binding Domain of SARS-CoV-2

The novel coronavirus disease 2019 (COVID-19) is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which initially appeared in Wuhan, China, in December 2019. Elderly individuals and those with comorbid conditions may be more vulnerable to this disease. Consequently, several research laboratories continue to focus on developing drugs to treat this infection because this disease has developed into a global pandemic with an extremely limited number of specific treatments available. Natural herbal remedies have long been used to treat illnesses in a variety of cultures. Modern medicine has achieved success due to the effectiveness of traditional medicines, which are derived from medicinal plants. The objective of this study was to determine whether components of natural origin from Iranian medicinal plants have an antiviral effect that can prevent humans from this coronavirus infection using the most reliable molecular docking method; in our case, we focused on the main protease (Mpro) and a receptor-binding domain (RBD). The results of molecular docking showed that among 169 molecules of natural origin from common Iranian medicinal plants, 20 molecules (chelidimerine, rutin, fumariline, catechin gallate, adlumidine, astragalin, somniferine, etc.) can be proposed as inhibitors against this coronavirus based on the binding free energy and type of interactions between these molecules and the studied proteins. Moreover, a molecular dynamics simulation study revealed that the chelidimerine-Mpro and somniferine-RBD complexes were stable for up to 50 ns below 0.5 nm. Our results provide valuable insights into this mechanism, which sheds light on future structure-based designs of high-potency inhibitors for SARS-CoV-2.

Identification of non-classical hCA XII inhibitors using combination of computational approaches for drug design and discovery

Human carbonic anhydrase XII (hCA XII) isozyme is of high therapeutic value as a pharmacological target and biomarker for different types of cancer. The hCA XII is one of the crucial effectors that regulates extracellular and intracellular pH and affects cancer cell proliferation, invasion, growth and metastasis. Despite the fact that interaction features of hCAs inhibitors with the catalytic site of the enzyme are well described, lack in the selectivity of the traditional hCA inhibitors based on the sulfonamide group or related motifs is an urgent issue. Moreover, drugs containing sulfanomides can cause sulfa allergies. Thus, identification of novel non-classical inhibitors of hCA XII is of high priority and is currently the subject of a vast field of study. This study was devoted to the identification of novel potential hCA XII inhibitors using comprehensive set of computational approaches for drug design discovery: generation and validation of structure- and ligand-based pharmacophore models, molecular docking, re-scoring of virtual screening results with MMGBSA, molecular dynamics simulations, etc. As the results of the study several compounds with alternative to classical inhibitors chemical scaffolds, in particular one of coumarins derivative, have been identified and are of high interest as potential non-classical hCA XII inhibitors.

A structure-based approach for the discovery of inhibitors against methylcitrate synthase of Paracoccidioides lutzii

Paracoccidioidomycosis (PCM) is a systemic mycosis, endemic in Latin America, caused by fungi of the genus Paracoccidioides. The treatment of PCM is complex, requiring a long treatment period, which often results in serious side effects. The aim of this study was to screen for inhibitors of a specific target of the fungus that is absent in humans. Methylcitrate synthase (MCS) is a unique enzyme of microorganisms and is responsible for the synthesis of methylcitrate at the beginning of the propionate degradation pathway. This pathway is essential for several microorganisms, since the accumulation of propionyl-CoA can impair virulence and prevent the development of the pathogen. We performed the modeling and molecular dynamics of the structure of Paracoccidioides lutzii MCS (PlMCS) and performed a virtual screening on 89,415 compounds against the active site of the enzyme. The compounds were selected according to the affinity and efficiency criteria of in vitro tests. Six compounds were able to inhibit the enzymatic activity of recombinant PlMCS but only the compound ZINC08964784 showed fungistatic and fungicidal activity against Paracoccidioides spp. cells. The analysis of the interaction profile of this compound with PlMCS showed its effectiveness in terms of specificity and stability when compared to the substrate (propionyl-CoA) of the enzyme. In addition, this compound did not show cytotoxicity in mammalian cells, with an excellent selectivity index. Our results suggest that the compound ZINC08964784 may become a promising alternative antifungal against Paracoccidioides spp. Communicated by Ramaswamy H. Sarma.

Potential molecular targets of nonstructural proteins for the development of antiviral drugs against SARS-CoV-2 infection

•

The pandemic of SARS-CoV-2 has posed significant threats to public health worldwide.

•

Target-based drug development is a promising approach against SARS-CoV-2 infection.

•

Nonstructural proteins may play critical roles from drug design perspectives.

•

Insights into NSPs of different viruses could streamline novel drug development.

Outbreaks of severe acute respiratory syndrome coronavirus (SARS-CoV), Middle East respiratory syndrome coronavirus (MERS-CoV), and SARS-CoV-2 have produced high pathogenicity and mortality rates in human populations. However, to meet the increasing demand for treatment of these pathogenic coronaviruses, accelerating novel antiviral drug development as much as possible has become a public concern. Target-based drug development may be a promising approach to achieve this goal. In this review, the relevant features of potential molecular targets in human coronaviruses (HCoVs) are highlighted, including the viral protease, RNA-dependent RNA polymerase, and methyltransferases. Additionally, recent advances in the development of antivirals based on these targets are summarized. This review is expected to provide new insights and potential strategies for the development of novel antiviral drugs to treat SARS-CoV-2 infection.

Artificial Intelligence and Machine Learning in Computational Nanotoxicology: Unlocking and Empowering Nanomedicine

Advances in nanomedicine, coupled with novel methods of creating advanced materials at the nanoscale, have opened new perspectives for the development of healthcare and medical products. Special attention must be paid toward safe design approaches for nanomaterial-based products. Recently, artificial intelligence (AI) and machine learning (ML) gifted the computational tool for enhancing and improving the simulation and modeling process for nanotoxicology and nanotherapeutics. In particular, the correlation of in vitro generated pharmacokinetics and pharmacodynamics to in vivo application scenarios is an important step toward the development of safe nanomedicinal products. This review portrays how in vitro and in vivo datasets are used in in silico models to unlock and empower nanomedicine. Physiologically based pharmacokinetic (PBPK) modeling and absorption, distribution, metabolism, and excretion (ADME)-based in silico methods along with dosimetry models as a focus area for nanomedicine are mainly described. The computational OMICS, colloidal particle determination, and algorithms to establish dosimetry for inhalation toxicology, and quantitative structure-activity relationships at nanoscale (nano-QSAR) are revisited. The challenges and opportunities facing the blind spots in nanotoxicology in this computationally dominated era are highlighted as the future to accelerate nanomedicine clinical translation.

Proposing the Promiscuous Protein Structures in JNK1 and JNK3 for Virtual Screening in Pursuit of Potential Leads

Over the past decade, the available crystal structures have almost doubled in Protein Data Bank (PDB) providing the research community with a series of similar crystal structures to choose from for future docking studies. With the steady growth in the number of high-resolution three-dimensional protein structures, ligand docking-based virtual screening of chemical libraries to a receptor plays a critical role in the drug discovery process by identifying new drug candidates. Thus, identifying potential candidates among all the available structures in a database for docking studies is of utmost importance. Our work examined whether one could use the resolution of a number of known structures, without considering other parameters, to choose a good experimental structure for various docking studies to find more useful drug leads. We expected that a good experimental structure for docking studies to be the one that gave favorable docking with the largest number of ligands among the experimental structures to be selected. We chose three protein test systems for our study, all belonging to the family of MAPK: (1) JNK1, (2) JNK2, and (3) JNK3. On analysis of the results, the best resolution structures showed significant variations from the expected values in their result, whereas the poor resolution structures proved to be better candidates for docking studies.

Identification of the potential biological target of N-benzenesulfonyl-1,2,3,4-tetrahydroquinoline compounds active against gram-positive and gram-negative bacteria

The development of new antibiotics with activity towards a broad spectrum of bacteria, including multiresistant strains, is a very important topic for global public health. As part of previous works, N-benzenesulfonyl-1,2,3,4-tetrahydroquinoline (BSTHQ) derivatives were described as antimicrobial agents active against gram-positive and gram-negative pathogens. In this work, experimental and molecular modelling studies were performed in order to identify their potential biological target in the light of structure-based design efforts towards further BSTHQ derivatives. First, a carboxyfluorescein leakage assay was performed using liposomes to mimic bacterial membranes, which found no significative membrane disruption effects with respect to control samples. These results support a non-surfactant antimicrobial activity of the tested compounds. In a second stage, the inhibition of potential antimicrobial targets was screened using molecular modelling methods, taking into account previously reported druggable targets deposited in the ChEMBL database for Escherichia coli and Staphylococcus aureus. Two enzymes, namely D-glutamic acid-adding enzyme (MurD) and N-acetylglucosamine-1-phophate-uridyltransferase (GlmU), both involved in bacterial membrane synthesis, were identified as potential targets. Pharmacodynamic interaction models were developed using known MurD and GlmU inhibitors by applying state-of-the-art chemoinformatic methods (molecular docking, molecular dynamics and free energy of interaction analyses). These models were further extended to the analysis of the studied BSTHQ derivatives. Overall, our results demonstrated that the studied BSTHQ derivatives elicit their antibacterial activity by interacting with a specific molecular target, GlmU being the highly feasible one. Based on the presented results, further structure-aided design efforts towards the obtaining of novel BSTHQ derivatives are envisioned.Communicated by Ramaswamy H. Sarma.