Advancing computer-aided drug discovery (CADD) by big data and data-driven machine learning modeling

Development of a novel representation of drug 3D structures and enhancement of the TSR-based method for probing drug and target interactions

Understanding the mechanisms underlying interactions between drugs and target proteins is critical for drug discovery. In our earlier studies, we introduced the Triangular Spatial Relationship (TSR)-based algorithm, which enables the representation of a protein's 3D structure as a vector of integers (TSR keys). These TSR keys correspond to substructures of the 3D structure of a protein and are computed based on the triangles constructed by all possible triples of Cα atoms within the protein. In this study, we report on a new TSR-based algorithm for probing drug and target interactions. Specifically, we have extended the previous algorithm in three novel directions: TSR keys for representing the 3D structure of a drug or a ligand, cross TSR keys between drugs and their targets and intra-residual TSR keys for phosphorylated amino acids. The outcomes illustrate the key contributions as follows: (i) The TSR-based method, which uses the TSR keys as features, is unique in its capability to interpret hierarchical relationships of drugs as well as drug - target complexes using common and specific TSR keys. (ii) The method can distinguish not only the binding sites from the rest of the protein structures, but also the binding sites of primary targets from those of off-targets. (iii) The method has the potential to correlate the 3D structures of drugs with their functions. (iv) Representation of 3D structures by TSR keys has its unique advantage in terms of ease of making searching for similar substructures across structure datasets easier. In summary, this study presents a novel computational methodology, with significant advantages, for providing insights into the mechanism underlying drug and target interactions.

Exploration of alpha-glucosidase inhibitors: A comprehensive in silico approach targeting a large set of triazole derivatives

BACKGROUND

The increasing prevalence of diabetes and the side effects associated with current medications necessitate the development of novel candidate drugs targeting alpha-glucosidase as a potential treatment option.

METHODS

This study employed computer-aided drug design techniques to identify potential alpha-glucosidase inhibitors from the PubChem database. Molecular docking was used to evaluate 81,197 compounds, narrowing the set for further analysis and providing insights into ligand-target interactions. An ADMET study assessed the pharmacokinetic properties of these compounds, including absorption, distribution, metabolism, excretion, and toxicity. Molecular dynamics simulations validated the docking results.

RESULTS

9 compounds were identified as potential candidate drugs based on their ability to form stable complexes with alpha-glucosidase and their favorable pharmacokinetic profiles, three of these compounds were subjected to the molecular dynamics, which showed stability throughout the entire 100 ns simulation.

CONCLUSION

These findings suggest promising new alpha-glucosidase inhibitors for diabetes treatment. Further validation through in vitro and in vivo studies is recommended to confirm their efficacy and safety.

In silico exploration of PD-L1 binding compounds: Structure-based virtual screening, molecular docking, and MD simulation

Programmed death-ligand 1 (PD-L1), a transmembrane protein, is associated with the regulation of immune system. It frequently has overexpression in various cancers, allowing tumor cells to avoid immune detection. PD-L1 inhibition has risen as a potential strategy in the field of therapeutic immunology for cancer. In the current study, structure-based virtual screening of drug libraries was conducted and then the screened hits were docked to the active residues of PD-L1 to select the optimal binding poses. The top ten compounds with binding affinities ranging from -10.734 to -10.398 kcal/mol were selected for further analysis. The ADMET analysis of selected compounds showed the compounds meet the criteria of ADMET properties. Further, the conformational changes and binding stability of the top two compounds was analyzed by conducting 200 ns simulation and it was observed that the hits did not exert conformational changes to the protein structure. All the results suggest that the chosen hits can be considered as lead compounds for the inhibition of biological activity of PD-L1 in in vitro studies.

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.

Structure-based pharmacophore modeling for precision inhibition of mutant ESR2 in breast cancer: A systematic computational approach

BACKGROUND

Breast cancer, a leading cause of female mortality, is closely linked to mutations in estrogen receptor beta (ESR2), particularly in the ligand-binding domain, which contributed to altered signaling pathways and uncontrolled cell growth.

OBJECTIVES/AIMS

This study investigates the molecular and structural aspects of ESR2 mutant proteins to identify shared pharmacophoric regions of ESR2 mutant proteins and potential therapeutic targets aligned within the pharmacophore model.

METHODS

This study was initiated by establishing a common pharmacophore model among three mutant ESR2 proteins (PDB ID: 2FSZ, 7XVZ, and 7XWR). The generated shared feature pharmacophore (SFP) includes four primary binding interactions: Hydrogen bond donors (HBD), hydrogen bond acceptors (HBA), hydrophobic interactions (HPho), and Aromatic interactions (Ar), along with halogen bond donors (XBD) and totalling 11 features (HBD: 2, HBA: 3, HPho: 3, Ar: 2, XBD: 1). By employing an in-house Python script, these 11 features distributed into 336 combinations, which were used as query to isolate a drug library of 41,248 compounds and subjected to virtual screening through the generated SFP.

RESULTS

The virtual screening demonstrated 33 hits showing potential pharmacophoric fit scores and low RMSD value. The top four compounds: ZINC94272748, ZINC79046938, ZINC05925939, and ZINC59928516 showed a fit score of more than 86% and satisfied the Lipinski rule of five. These four compounds and a control underwent molecular (XP Glide mode) docking analysis against wild-type ESR2 protein (PDB ID: 1QKM), resulting in binding affinity of -8.26, -5.73, -10.80, and -8.42 kcal/mol, respectively, along with the control -7.2 kcal/mol. Furthermore, the stability of the selected candidates was determined through molecular dynamics (MD) simulations of 200 ns and MM-GBSA analysis.

CONCLUSION

Based on MD simulations and MM-GBSA analysis, our study identified ZINC05925939 as a promising ESR2 inhibitor among the top four hits. However, it is essential to conduct further wet lab evaluation to assess its efficacy.

Advances in artificial intelligence for drug delivery and development: A comprehensive review

Artificial intelligence (AI) has emerged as a powerful tool to revolutionize the healthcare sector, including drug delivery and development. This review explores the current and future applications of AI in the pharmaceutical industry, focusing on drug delivery and development. It covers various aspects such as smart drug delivery networks, sensors, drug repurposing, statistical modeling, and simulation of biotechnological and biological systems. The integration of AI with nanotechnologies and nanomedicines is also examined. AI offers significant advancements in drug discovery by efficiently identifying compounds, validating drug targets, streamlining drug structures, and prioritizing response templates. Techniques like data mining, multitask learning, and high-throughput screening contribute to better drug discovery and development innovations. The review discusses AI applications in drug formulation and delivery, clinical trials, drug safety, and pharmacovigilance. It addresses regulatory considerations and challenges associated with AI in pharmaceuticals, including privacy, data security, and interpretability of AI models. The review concludes with future perspectives, highlighting emerging trends, addressing limitations and biases in AI models, and emphasizing the importance of collaboration and knowledge sharing. It provides a comprehensive overview of AI's potential to transform the pharmaceutical industry and improve patient care while identifying further research and development areas.

Predicting Chemical Immunotoxicity through Data-Driven QSAR Modeling of Aryl Hydrocarbon Receptor Agonism and Related Toxicity Mechanisms

Computational modeling has emerged as a time-saving and cost-effective alternative to traditional animal testing for assessing chemicals for their potential hazards. However, few computational modeling studies for immunotoxicity were reported, with few models available for predicting toxicants due to the lack of training data and the complex mechanisms of immunotoxicity. In this study, we employed a data-driven quantitative structure-activity relationship (QSAR) modeling workflow to extensively enlarge the limited training data by revealing multiple targets involved in immunotoxicity. To this end, a probe data set of 6,341 chemicals was obtained from a high-throughput screening (HTS) assay testing for the activation of the aryl hydrocarbon receptor (AhR) signaling pathway, a key event leading to immunotoxicity. Searching this probe data set against PubChem yielded 3,183 assays with testing results for varying proportions of these 6,341 compounds. 100 assays were selected to develop QSAR models based on their correlations to AhR agonism. Twelve individual QSAR models were built for each assay using combinations of four machine-learning algorithms and three molecular fingerprints. 5-fold cross-validation of the resulting models showed good predictivity (average CCR = 0.73). A total of 20 assays were further selected based on QSAR model performance, and their resulting QSAR models showed good predictivity of potential immunotoxicants from external chemicals. This study provides a computational modeling strategy that can utilize large public toxicity data sets for modeling immunotoxicity and other toxicity endpoints, which have limited training data and complicated toxicity mechanisms.

Next Generation Risk Assessment approaches for advanced nanomaterials: Current status and future perspectives

This manuscript discusses the challenges of applying New Approach Methodologies (NAMs) for safe by design and regulatory risk assessment of advanced nanomaterials (AdNMs). The authors propose a framework for Next Generation Risk Assessment of AdNMs involving NAMs that is aligned to the conventional risk assessment paradigm. This framework is exposure-driven, endpoint-specific, makes best use of pre-existing information, and can be implemented in tiers of increasing specificity and complexity of the adopted NAMs. The tiered structure of the approach, which effectively combines the use of existing data with targeted testing will allow safety to be assessed cost-effectively and as far as possible with even more limited use of vertebrates. The regulatory readiness of state-of-the-art emerging NAMs is assessed in terms of Transparency, Reliability, Accessibility, Applicability, Relevance and Completeness, and their appropriateness for AdNMs is discussed in relation to each step of the risk assessment paradigm along with providing perspectives for future developments in the respective scientific and regulatory areas.

Synergizing Chemical Structures and Bioassay Descriptions for Enhanced Molecular Property Prediction in Drug Discovery

The precise prediction of molecular properties can greatly accelerate the development of new drugs. However, in silico molecular property prediction approaches have been limited so far to assays for which large amounts of data are available. In this study, we develop a new computational approach leveraging both the textual description of the assay of interest and the chemical structure of target compounds. By combining these two sources of information via self-supervised learning, our tool can provide accurate predictions for assays where no measurements are available. Remarkably, our approach achieves state-of-the-art performance on the FS-Mol benchmark for zero-shot prediction, outperforming a wide variety of deep learning approaches. Additionally, we demonstrate how our tool can be used for tailoring screening libraries for the assay of interest, showing promising performance in a retrospective case study on a high-throughput screening campaign. By accelerating the early identification of active molecules in drug discovery and development, this method has the potential to streamline the identification of novel therapeutics.

Exploring the conformational dynamics and key amino acids in the CD26-caveolin-1 interaction and potential therapeutic interventions

This study aimed to decipher the interaction between CD26 and caveolin-1, key proteins involved in cell signaling and linked to various diseases. Using computational methods, we predicted their binding conformations and assessed stability through 100 ns molecular dynamics (MD) simulations. We identified two distinct binding conformations (con1 and con4), with con1 exhibiting superior stability. In con1, specific amino acids in CD26, namely GLU237, TYR241, TYR248, and ARG147, were observed to engage in interactions with the F-J chain of Caveolin-1, establishing hydrogen bonds and cation or π-π interactions. Meanwhile, in con4, CD26 amino acids ARG253, LYS250, and TYR248 interacted with the J chain of Caveolin-1 via hydrogen bonds, cation-π interactions, and π-π interactions. Virtual screening also revealed potential small-molecule modulators, including Crocin, Poliumoside, and Canagliflozin, that could impact this interaction. Additionally, predictive analyses were conducted on the potential bioactivity, drug-likeness, and ADMET properties of these three compounds. These findings offer valuable insights into the binding mechanism, paving the way for new therapeutic strategies. However, further validation is required before clinical application. In summary, we provide a detailed understanding of the CD26 and caveolin-1 interaction, identifying key amino acids and potential modulators, essential for developing targeted therapies.

Discovery of Novel Aldose Reductase Inhibitors via the Integration of Ligand-Based and Structure-Based Virtual Screening with Experimental Validation

Aldose reductase plays a central role in diabetes mellitus (DM) associated complications by converting glucose to sorbitol, resulting in a harmful increase of reactive oxygen species (ROS) in various tissues, such as the heart, vasculature, neurons, eyes, and kidneys. We employed a comprehensive approach, integrating both ligand- and structure-based virtual screening followed by experimental validation. Initially, candidate compounds were extracted from extensive drug and chemical libraries using the DeepChem's GraphConvMol algorithm, leveraging its capacity for robust molecular feature representation. Subsequent refinement employed molecular docking and molecular dynamics (MD) simulations, which are crucial for understanding compound-receptor interactions and dynamic behavior in a simulated physiological environment. Finally, the candidate compounds were subjected to experimental validation of their biological activity using an aldose reductase inhibitor screening kit. The comprehensive approach led to the identification of a promising compound, demonstrating significant potential as an aldose reductase inhibitor. This comprehensive approach not only yields a potential therapeutic intervention for DM-related complications but also establishes an integrated protocol for drug development, setting a new benchmark in the field.

Advances in machine intelligence-driven virtual screening approaches for big-data

Virtual screening (VS) is an integral and ever-evolving domain of drug discovery framework. The VS is traditionally classified into ligand-based (LB) and structure-based (SB) approaches. Machine intelligence or artificial intelligence has wide applications in the drug discovery domain to reduce time and resource consumption. In combination with machine intelligence algorithms, VS has emerged into revolutionarily progressive technology that learns within robust decision orders for data curation and hit molecule screening from large VS libraries in minutes or hours. The exponential growth of chemical and biological data has evolved as "big-data" in the public domain demands modern and advanced machine intelligence-driven VS approaches to screen hit molecules from ultra-large VS libraries. VS has evolved from an individual approach (LB and SB) to integrated LB and SB techniques to explore various ligand and target protein aspects for the enhanced rate of appropriate hit molecule prediction. Current trends demand advanced and intelligent solutions to handle enormous data in drug discovery domain for screening and optimizing hits or lead with fewer or no false positive hits. Following the big-data drift and tremendous growth in computational architecture, we presented this review. Here, the article categorized and emphasized individual VS techniques, detailed literature presented for machine learning implementation, modern machine intelligence approaches, and limitations and deliberated the future prospects.

Directional ΔG Neural Network (DrΔG-Net): A Modular Neural Network Approach to Binding Free Energy Prediction

The protein-ligand binding free energy is a central quantity in structure-based computational drug discovery efforts. Although popular alchemical methods provide sound statistical means of computing the binding free energy of a large breadth of systems, they are generally too costly to be applied at the same frequency as end point or ligand-based methods. By contrast, these data-driven approaches are typically fast enough to address thousands of systems but with reduced transferability to unseen systems. We introduce DrΔG-Net (or simply Dragnet), an equivariant graph neural network that can blend ligand-based and protein-ligand data-driven approaches. It is based on a 3D fingerprint representation of the ligand alone and in complex with the protein target. Dragnet is a global scoring function to predict the binding affinity of arbitrary protein-ligand complexes, but can be easily tuned via transfer learning to specific systems or end points, performing similarly to common 2D ligand-based approaches in these tasks. Dragnet is evaluated on a total of 28 validation proteins with a set of congeneric ligands derived from the Binding DB and one custom set extracted from the ChEMBL Database. In general, a handful of experimental binding affinities are sufficient to optimize the scoring function for a particular protein and ligand scaffold. When not available, predictions from physics-based methods such as absolute free energy perturbation can be used for the transfer learning tuning of Dragnet. Furthermore, we use our data to illustrate the present limitations of data-driven modeling of binding free energy predictions.

Computer aided drug discovery (CADD) of a thieno[2,3-d]pyrimidine derivative as a new EGFR inhibitor targeting the ribose pocket

Depending on the pharmacophoric characteristics of EGFR inhibitors, a new thieno[2,3-d]pyrimidine derivative has been developed. Firstly, the potential inhibitory effect of the designed compound against EGFR has been proven by docking experiments that showed correct binding modes and excellent binding energies of -98.44 and -88.00 kcal/mol, against EGFR wild-type and mutant type, respectively. Furthermore, MD simulations studies confirmed the precise energetic, conformational, and dynamic alterations that occurred after binding to EGFR. The correct binding was also confirmed by essential dynamics studies. To further investigate the general drug-like properties of the developed candidate, in silico ADME and toxicity studies have also been carried out. The thieno[2,3-d]pyrimidine derivative was synthesized following the earlier promising findings. Fascinatingly, the synthesized compound (4) showed promising inhibitory effects against EGFRWT and EGFRT790M with IC50 values of 25.8 and 182.3 nM, respectively. Also, it exhibited anticancer potentialities against A549 and MCF-7cell lines with IC50 values of 13.06 and 20.13 µM, respectively. Interestingly, these strong activities were combined with selectivity indices of 2.8 and 1.8 against the two cancer cell lines, respectively. Further investigations indicated the ability of compound 4 to arrest the cancer cells' growth at the G2/M phase and to increase early and late apoptosis percentages from 2.52% and 2.80 to 17.99% and 16.72%, respectively. Additionally, it was observed that compound 4 markedly increased the levels of caspase-3 and caspase-9 by 4 and 3-fold compared to the control cells. Moreover, it up-regulated the level of BAX by 3-fold and down-regulated the level of Bcl-2 by 3-fold affording a BAX/Bcl-2 ratio of 9.Communicated by Ramaswamy H. Sarma.

Chemical library design, QSAR modeling and molecular dynamics simulations of naturally occurring coumarins as dual inhibitors of MAO-B and AChE

Coumarins are a highly privileged scaffold in medicinal chemistry. It is present in many natural products and is reported to display various pharmacological properties. A large plethora of compounds based on the coumarin ring system have been synthesized and were found to possess biological activities such as anticonvulsant, antiviral, anti-inflammatory, antibacterial, antioxidant as well as neuroprotective properties. Despite the wide activity spectrum of coumarins, its naturally occurring derivatives are yet to be investigated in detail. In the current study, a chemical library was created to assemble all chemical information related to naturally occurring coumarins from the literature. Additionally, a multi-stage virtual screening combining QSAR modeling, molecular docking, and ADMET prediction was conducted against monoamine oxidase B and acetylcholinesterase, two relevant targets known for their neuroprotective properties and 'disease-modifying' potential in Parkinson's and Alzheimer's disease. Our findings revealed ten coumarin derivatives that may act as dual-target drugs against MAO-B and AChE. Two coumarin candidates were selected from the molecular docking study: CDB0738 and CDB0046 displayed favorable interactions for both proteins as well as suitable ADMET profiles. The stability of the selected coumarins was assessed through 100 ns molecular dynamics simulations which revealed promising stability through key molecular interactions for CDB0738 to act as dual inhibitor of MAO-B and AChE. However, experimental studies are necessary to evaluate the bioactivity of the proposed candidate. The current results may generate an increasing interest in bioprospecting naturally occurring coumarins as potential candidates against relevant macromolecular targets by encouraging virtual screening studies against our chemical library.Communicated by Ramaswamy H. Sarma.

Leveraging Artificial Intelligence for Synergies in Drug Discovery: From Computers to Clinics

Over the period of the preceding decade, artificial intelligence (AI) has proved an outstanding performance in entire dimensions of science including pharmaceutical sciences. AI uses the concept of machine learning (ML), deep learning (DL), and neural networks (NNs) approaches for novel algorithm and hypothesis development by training the machines in multiple ways. AI-based drug development from molecule identification to clinical approval tremendously reduces the cost of development and the time over conventional methods. The COVID-19 vaccine development and approval by regulatory agencies within 1-2 years is the finest example of drug development. Hence, AI is fast becoming a boon for scientific researchers to streamline their advanced discoveries. AI-based FDA-approved nanomedicines perform well as target selective, synergistic therapies, recolonize the theragnostic pharmaceutical stream, and significantly improve drug research outcomes. This comprehensive review delves into the fundamental aspects of AI along with its applications in the realm of pharmaceutical life sciences. It explores AI's role in crucial areas such as drug designing, drug discovery and development, traditional Chinese medicine, integration of multi-omics data, as well as investigations into drug repurposing and polypharmacology studies.

A New Optimized Hybridization Approach for in silico High Throughput Molecular Docking on FPGA Platform

BACKGROUND

The development process of a new drug should be a subject of continuous evolution and rapid improvement as drugs are essential to treat a wide range of diseases of which many are life-threatening. The advances in technology resulted in a novel track in drug discovery and development known as in silico drug design. The molecular docking phase plays a vital role in in silico drug development process. In this phase, thousands of 3D conformations of both the ligand and receptor are generated and the best conformations that create the most stable drug-receptor complex are determined. The speed in finding accurate and high-quality complexes depends on the efficiency of the search function in the molecular docking procedure.

OBJECTIVE

The objective of this research is to propose and implement a novel hybrid approach called hABCDE to replace the EMC searching part inside the BUDE docking algorithm. This helps in reaching the best solution in a much accelerated time and higher solution quality compared to using the ABC and DE algorithms separately.

METHODS

In this work, we have employed a new approach of hybridization between the Artificial Bee Colony (ABC) algorithm and the Differential Evolution (DE) algorithm as an alternative searching part of the Bristol University Docking Engine (BUDE) in order to accelerate the search for higher quality solutions. Moreover, the proposed docking approach was implemented on Field Programmable Gate Array (FPGA) parallel platform using Vivado High-Level Synthesis Tool (HLST) in order to optimize and enhance the execution time and overall efficiency. The NDM-1 protein was used as a model receptor in our experiments to demonstrate the efficiency of our approach.

RESULTS

The NDM-1 protein was used as a model receptor in our experiments to demonstrate the efficiency of our approach. The results showed that the execution time for the BUDE with the new proposed hybridization approach was improved by 9,236 times.

CONCLUSION

Our novel approach was significantly effective to improve the functionality of docking algorithms (Bristol University Docking Engine (BUDE)).

Automation of Drug Discovery through Cutting-edge In-silico Research in Pharmaceuticals: Challenges and Future Scope

The rapidity and high-throughput nature of in silico technologies make them advantageous for predicting the properties of a large array of substances. In silico approaches can be used for compounds intended for synthesis at the beginning of drug development when there is either no or very little compound available. In silico approaches can be used for impurities or degradation products. Quantifying drugs and related substances (RS) with pharmaceutical drug analysis (PDA) can also improve drug discovery (DD) by providing additional avenues to pursue. Potential future applications of PDA include combining it with other methods to make insilico predictions about drugs and RS. One possible outcome of this is a determination of the drug potential of nontoxic RS. ADME estimation, QSAR research, molecular docking, bioactivity prediction, and toxicity testing all involve impurity profiling. Before committing to DD, RS with minimal toxicity can be utilised in silico. The efficacy of molecular docking in getting a medication to market is still debated despite its refinement and improvement. Biomedical labs and pharmaceutical companies were hesitant to adopt molecular docking algorithms for drug screening despite their decades of development and improvement. Despite the widespread use of "force fields" to represent the energy exerted within and between molecules, it has been impossible to reliably predict or compute the binding affinities between proteins and potential binding medications.

Machine learning in Alzheimer's disease drug discovery and target identification

Alzheimer's disease (AD) stands as a formidable neurodegenerative ailment that poses a substantial threat to the elderly population, with no known curative or disease-slowing drugs in existence. Among the vital and time-consuming stages in the drug discovery process, disease modeling and target identification hold particular significance. Disease modeling allows for a deeper comprehension of disease progression mechanisms and potential therapeutic avenues. On the other hand, target identification serves as the foundational step in drug development, exerting a profound influence on all subsequent phases and ultimately determining the success rate of drug development endeavors. Machine learning (ML) techniques have ushered in transformative breakthroughs in the realm of target discovery. Leveraging the strengths of large dataset analysis, multifaceted data processing, and the exploration of intricate biological mechanisms, ML has become instrumental in the quest for effective AD treatments. In this comprehensive review, we offer an account of how ML methodologies are being deployed in the pursuit of drug discovery for AD. Furthermore, we provide an overview of the utilization of ML in uncovering potential intervention strategies and prospective therapeutic targets for AD. Finally, we discuss the principal challenges and limitations currently faced by these approaches. We also explore the avenues for future research that hold promise in addressing these challenges.

In silico protein function prediction: the rise of machine learning-based approaches

Proteins function as integral actors in essential life processes, rendering the realm of protein research a fundamental domain that possesses the potential to propel advancements in pharmaceuticals and disease investigation. Within the context of protein research, an imperious demand arises to uncover protein functionalities and untangle intricate mechanistic underpinnings. Due to the exorbitant costs and limited throughput inherent in experimental investigations, computational models offer a promising alternative to accelerate protein function annotation. In recent years, protein pre-training models have exhibited noteworthy advancement across multiple prediction tasks. This advancement highlights a notable prospect for effectively tackling the intricate downstream task associated with protein function prediction. In this review, we elucidate the historical evolution and research paradigms of computational methods for predicting protein function. Subsequently, we summarize the progress in protein and molecule representation as well as feature extraction techniques. Furthermore, we assess the performance of machine learning-based algorithms across various objectives in protein function prediction, thereby offering a comprehensive perspective on the progress within this field.

Machine Learning-Based Drug Repositioning of Novel Janus Kinase 2 Inhibitors Utilizing Molecular Docking and Molecular Dynamic Simulation

Machine learning algorithms have been increasingly applied in drug development due to their efficiency and effectiveness. Machine learning-based drug repurposing can contribute to the identification of novel therapeutic applications for drugs with other indications. The current study used a trained machine learning model to screen a vast chemical library for new JAK2 inhibitors, the biological activities of which were reported. Reference JAK2 inhibitors, comprising 1911 compounds, have experimentally determined IC50 values. To generate the input to the machine learning model, reference compounds were subjected to RDKit, a cheminformatic toolkit, to extract molecular descriptors. A Random Forest Regression model from the Scikit-learn machine learning library was applied to obtain a predictive regression model and to analyze each molecular descriptor's role in determining IC50 values in the reference data set. Then, IC50 values of the library compounds, comprised of 1,576,903 compounds, were predicted using the generated regression model. Interestingly, some compounds that exhibit high IC50 values from the prediction were reported to possess JAK inhibition activity, which indicates the limitations of the prediction model. To confirm the JAK2 inhibition activity of predicted compounds, molecular docking and molecular dynamics simulation were carried out with the JAK inhibitor reference compound, tofacitinib. The binding affinity of docked compounds in the active region of JAK2 was also analyzed by the gmxMMPBSA approach. Furthermore, experimental validation confirmed the results from the computational analysis. Results showed highly comparable outcomes concerning tofacitinib. Conclusively, the machine learning model can efficiently improve the virtual screening of drugs and drug development.

A Theobromine Derivative with Anticancer Properties Targeting VEGFR-2: Semisynthesis, in silico and in vitro Studies

A computer-assisted drug design (CADD) approach was utilized to design a new acetamido-N-(para-fluorophenyl)benzamide) derivative of the naturally occurring alkaloid, theobromine, (T-1-APFPB), following the pharmacophoric features of VEGFR-2 inhibitors. The stability and reactivity of T-1-AFPB were assessed through density functional theory (DFT) calculations. Molecular docking assessments showed T-1-AFPB's potential to bind with and inhibit VEGFR-2. The precise binding of T-1-AFPB against VEGFR-2 with optimal energy was further confirmed through several molecular dynamics (MD) simulations, PLIP, MM-GBSA, and PCA studies. Then, T-1-AFPB (4-(2-(3,7-Dimethyl-2,6-dioxo-2,3,6,7-tetrahydro-1H-purin-1-yl)acetamido)-N-(4-fluorophenyl)benzamide) was semi-synthesized and the in vitro assays showed its potential to inhibit VEGFR-2 with an IC50 value of 69 nM (sorafenib's IC50 was 56 nM) and to inhibit the growth of HepG2 and MCF-7 cancer cell lines with IC50 values of 2.24±0.02 and 3.26±0.02 μM, respectively. Moreover, T-1-AFPB displayed very high selectivity indices against normal Vero cell lines. Furthermore, T-1-AFPB induced early (from 0.72 to 19.12) and late (from 0.13 to 6.37) apoptosis in HepG2 cell lines. In conclusion, the combined computational and experimental approaches demonstrated the efficacy and safety of T-1-APFPB providing it as a promising lead VEGFR-2 inhibitor for further development aiming at cancer therapy.

Structure-based virtual screening of natural compounds as inhibitors of HCV using molecular docking and molecular dynamics simulation studies

The hepatitis C virus (HCV), which causes hepatitis C, is a viral infection that damages the liver and causes inflammation in the liver. New potentially effective antiviral drugs are required for its treatment owing to various issues associated with the existing medications, including moderate to severe adverse effects, higher costs, and the emergence of drug-resistant strains. The objective of the current study was to utilize computational techniques to assess the anti-HCV efficacy of certain phytochemicals against tetraspanin (CD81) and claudin 1 (CLDN1) entry proteins. A 200-nanosecond molecular dynamics (MD) simulation was employed to examine the stability of the lead-protein complexes. Free binding energy and molecular docking calculations were conducted utilizing MM/GBSA method, and the selectivity of hit compounds for CD81 and CLDN1 was determined. Five significant CD81 and CLDN1 inhibitors were identified: Petasiphenone, Silibinin, Tanshinone IIA, Taxifolin, and Topaquinone. The MM/GBSA analysis of the compounds revealed high free binding energies. All the identified compounds were stable within the CD81 and CLDN1 binding pockets. This study indicated the promising inhibitory potential of the identified compounds against CD81 and CLDN1 receptors and might develop into potential viral entry inhibitors. However, to validate the chemotherapeutic capabilities of the discovered leads extensive preclinical research is required.Communicated by Ramaswamy H. Sarma.

Vismodegib Identified as a Novel COX-2 Inhibitor via Deep-Learning-Based Drug Repositioning and Molecular Docking Analysis

Artificial intelligence algorithms have been increasingly applied in drug development due to their efficiency and effectiveness. Deep-learning-based drug repurposing can contribute to the identification of novel therapeutic applications for drugs with other indications. The current study used a trained deep-learning model to screen an FDA-approved drug library for novel COX-2 inhibitors. Reference COX-2 data sets, composed of active and decoy compounds, were obtained from the DUD-E database. To extract molecular features, compounds were subjected to RDKit, a cheminformatic toolkit. GraphConvMol, a graph convolutional network model from DeepChem, was applied to obtain a predictive model from the DUD-E data sets. Then, the COX-2 inhibitory potential of the FDA-approved drugs was predicted using the trained deep-learning model. Vismodegib, an anticancer agent that inhibits the hedgehog signaling pathway by binding to smoothened, was predicted to inhibit COX-2. Noticeably, some compounds that exhibit high potential from the prediction were known to be COX-2 inhibitors, indicating the prediction model's liability. To confirm the COX-2 inhibition activity of vismodegib, molecular docking was carried out with the reference compounds of the COX-2 inhibitor, celecoxib, and ibuprofen. Furthermore, the experimental examination of COX-2 inhibition was also carried out using a cell culture study. Results showed that vismodegib exhibited a highly comparable COX-2 inhibitory activity compared to celecoxib and ibuprofen. In conclusion, the deep-learning model can efficiently improve the virtual screening of drugs, and vismodegib can be used as a novel COX-2 inhibitor.

An Updated Review on Developing Small Molecule Kinase Inhibitors Using Computer-Aided Drug Design Approaches

Small molecule kinase inhibitors (SMKIs) are of heightened interest in the field of drug research and development. There are 79 (as of July 2023) small molecule kinase inhibitors that have been approved by the FDA and hundreds of kinase inhibitor candidates in clinical trials that have shed light on the treatment of some major diseases. As an important strategy in drug design, computer-aided drug design (CADD) plays an indispensable role in the discovery of SMKIs. CADD methods such as docking, molecular dynamic, quantum mechanics/molecular mechanics, pharmacophore, virtual screening, and quantitative structure-activity relationship have been applied to the design and optimization of small molecule kinase inhibitors. In this review, we provide an overview of recent advances in CADD and SMKIs and the application of CADD in the discovery of SMKIs.

A novel thieno[2,3-d]pyrimidine derivative inhibiting vascular endothelial growth factor receptor-2: A story of computer-aided drug discovery

Following the pharmacophoric features of vascular endothelial growth factor receptor 2 (VEGFR-2) inhibitors, a novel thieno[2,3-d]pyrimidine derivative has been designed and its activity against VEGFR-2 has been demonstrated by molecular docking studies that showed an accurate binding mode and an excellent binding energy. Furthermore, the recorded binding was confirmed by a series of molecular dynamics simulation studies, which also revealed precise energetic, conformational, and dynamic changes. Additionally, molecular mechanics with generalized Born and surface area solvation and polymer-induced liquid precursors studies were conducted and verified the results of the MD simulations. Next, in silico absorption, distribution, metabolism, excretion, and toxicity studies have also been conducted to examine the general drug-like nature of the designed candidate. According to the previous results, the thieno[2,3-d]pyrimidine derivative was synthesized. Fascinatingly, it inhibited VEGFR-2 (IC50  = 68.13 nM) and demonstrated strong inhibitory activity toward human liver (HepG2), and prostate (PC3) cell lines with IC50 values of 6.60 and 11.25 µM, respectively. As well, it was safe and showed a high selectivity index against normal cell lines (WI-38). Finally, the thieno[2,3-d]pyrimidine derivative arrested the growth of the HepG2 cells at the G2/M phase inducing both early and late apoptosis. These results were further confirmed through the ability of the thieno[2,3-d]pyrimidine derivative to induce significant changes in the apoptotic genes levels of caspase-3, caspase-9, Bcl-2 associated X-protein, and B-cell lymphoma 2.

Chemico-pharmacological and computational studies of Ophiorrhiza fasciculata D. Don and Psychotria silhetensis Hook. f. focusing cytotoxic, thrombolytic, anti-inflammatory, antioxidant, and antibacterial properties

The current study sought to examine the pharmacological potentials of crude methanolic extracts of Ophiorrhiza fasciculata and Psychotria silhetensis, as well as their various solvent fractionates, with a focus on cytotoxic, thrombolytic, membrane stabilizing, antioxidant, and antibacterial activities via in vitro and in silico approaches. The extensive chromatographic and spectroscopic analyses confirmed and characterized two compounds as (±)-licarin B (1) and stigmasterol (2) from O. fasciculata and P. silhetensis, respectively. Petroleum ether soluble fraction of O. fasciculata and the aqueous soluble fraction of P. silhetensis showed the lowest 50% lethal concentrations (1.41 and 1.94 μg/mL, respectively) in brine shrimp bioassay. Likewise, petroleum ether soluble fraction of O. fasciculata and aqueous soluble fraction of P. silhetensis showed the highest thrombolytic activity with 46.66% and 50.10% lyses of the clot, respectively. The methanol and dichloromethane soluble fractions of O. fasciculata reduced erythrocyte hemolysis by 64.03% and 37.08%, respectively, under hypotonic and heat-induced conditions, compared to 81.97% and 42.12% for standard acetylsalicylic acid. In antioxidant activity test, aqueous soluble fraction O. fasciculata (IC50 = 7.22 μg/mL) revealed promising antioxidant potentialities in comparison to standard butylated hydroxytoluene (IC50 = 21.20 μg/mL). In antibacterial screening, chloroform, and dichloromethane soluble fractions of P. silhetensis showed a mild antibacterial activity compared with the standard drug ciprofloxacin. Additionally, the molecular docking study corroborated the current in vitro findings, and the isolated two constituents had higher binding affinities toward epidermal growth factor receptor, tissue plasminogen activator, vFLIP-IKK gamma stapled peptide dimer, glutathione reductase, and dihydrofolate reductase enzyme than their corresponding standard drugs. In addition, the both isolated compounds exerted favorable pharmacokinetics (absorption, distribution, metabolism, excretion) and toxicological profiles with drug-like qualities in computational-based ADMET and drug likeliness analyses. The current research suggests that both plants have potential as a natural treatment for treating thrombosis, inflammation, and oxidative stress. However, more thorough research is required to thoroughly screen for phytochemicals and pinpoint the precise mechanisms of action of the bioactive metabolites derived from these plants against a broad range of molecular targets.

Exploring chemical space for lead identification by propagating on chemical similarity network

Motivation

Lead identification is a fundamental step to prioritize candidate compounds for downstream drug discovery process. Machine learning (ML) and deep learning (DL) approaches are widely used to identify lead compounds using both chemical property and experimental information. However, ML or DL methods rarely consider compound similarity information directly since ML and DL models use abstract representation of molecules for model construction. Alternatively, data mining approaches are also used to explore chemical space with drug candidates by screening undesirable compounds. A major challenge for data mining approaches is to develop efficient data mining methods that search large chemical space for desirable lead compounds with low false positive rate.

Results

In this work, we developed a network propagation (NP) based data mining method for lead identification that performs search on an ensemble of chemical similarity networks. We compiled 14 fingerprint-based similarity networks. Given a target protein of interest, we use a deep learning-based drug target interaction model to narrow down compound candidates and then we use network propagation to prioritize drug candidates that are highly correlated with drug activity score such as IC50. In an extensive experiment with BindingDB, we showed that our approach successfully discovered intentionally unlabeled compounds for given targets. To further demonstrate the prediction power of our approach, we identified 24 candidate leads for CLK1. Two out of five synthesizable candidates were experimentally validated in binding assays. In conclusion, our framework can be very useful for lead identification from very large compound databases such as ZINC.

Recent Advances in Toxicity Prediction: Applications of Deep Graph Learning

The development of new drugs is time-consuming and expensive, and as such, accurately predicting the potential toxicity of a drug candidate is crucial in ensuring its safety and efficacy. Recently, deep graph learning has become prevalent in this field due to its computational power and cost efficiency. Many novel deep graph learning methods aid toxicity prediction and further prompt drug development. This review aims to connect fundamental knowledge with burgeoning deep graph learning methods. We first summarize the essential components of deep graph learning models for toxicity prediction, including molecular descriptors, molecular representations, evaluation metrics, validation methods, and data sets. Furthermore, based on various graph-related representations of molecules, we introduce several representative studies and methods for toxicity prediction from the perspective of GNN architectures and graph pretrained models. Compared to other types of models, deep graph models not only advance in higher accuracy and efficiency but also provide more intuitive insights, which is significant in the development of model interpretation and generalization ability. The graph pretrained models are emerging as they can extract prominent features from large-scale unlabeled molecular graph data and improve the performance of downstream toxicity prediction tasks. We hope this survey can serve as a handbook for individuals interested in exploring deep graph learning for toxicity prediction.

Exploring and targeting potential druggable antimicrobial resistance targets ArgS, SecY, and MurA in Staphylococcus sciuri with TCM inhibitors through a subtractive genomics strategy

Staphylococcus sciuri (also currently Mammaliicoccus sciuri) are anaerobic facultative and non-motile bacteria that cause significant human pathogenesis such as endocarditis, wound infections, peritonitis, UTI, and septic shock. Methicillin-resistant S. sciuri (MRSS) strains also infects animals that include healthy broilers, cattle, dogs, and pigs. The emergence of MRSS strains thereby poses a serious health threat and thrives the scientific community towards novel treatment options. Herein, we investigated the druggable genome of S. sciuri by employing subtractive genomics that resulted in seven genes/proteins where only three of them were predicted as final targets. Further mining the literature showed that the ArgS (WP_058610923), SecY (WP_058611897), and MurA (WP_058612677) are involved in the multi-drug resistance phenomenon. After constructing and verifying the 3D protein homology models, a screening process was carried out using a library of Traditional Chinese Medicine compounds (consisting of 36,043 compounds). The molecular docking and simulation studies revealed the physicochemical stability parameters of the docked TCM inhibitors in the druggable cavities of each protein target by identifying their druggability potential and maximum hydrogen bonding interactions. The simulated receptor-ligand complexes showed the conformational changes and stability index of the secondary structure elements. The root mean square deviation (RMSD) graph showed fluctuations due to structural changes in the helix-coil-helix and beta-turn-beta changes at specific points where the pattern of the RMSD and root mean square fluctuation (RMSF) (< 1.0 Å) support any major domain shifts within the structural framework of the protein-ligand complex and placement of ligand was well complemented within the binding site. The β-factor values demonstrated instability at few points while the radius of gyration for structural compactness as a time function for the 100-ns simulation of protein-ligand complexes showed favorable average values and denoted the stability of all complexes. It is assumed that such findings might facilitate researchers to robustly discover and develop effective therapeutics against S. sciuri alongside other enteric infections.

A study of the interaction space of two lactate dehydrogenase isoforms (LDHA and LDHB) and some of their inhibitors using proteochemometrics modeling

Lactate dehydrogenase (LDH) is a tetramer enzyme that converts pyruvate to lactate reversibly. This enzyme becomes important because it is associated with diseases such as cancers, heart disease, liver problems, and most importantly, corona disease. As a system-based method, proteochemometrics does not require knowledge of the protein's three-dimensional structure, but rather depends on the amino acid sequence and protein descriptors. Here, we applied this methodology to model a set of LDHA and LDHB isoenzyme inhibitors. To implement the proteochemetrics method, the camb package in the R Studio Server programming environment was used. The activity of 312 compounds of LDHA and LDHB isoenzyme inhibitors from the valid Binding DB database was retrieved. The proteochemometrics method was applied to three machine learning algorithms gradient amplification model, random forest, and support vector machine as regression methods to find the best model. Through the combination of different models into an ensemble (greedy and stacking optimization), we explored the possibility of improving the performance of models. For the RF best ensemble model of inhibitors of LDHA and LDHB isoenzymes, and were 0.66 and 0.62, respectively. LDH inhibitory activation is influenced by Morgan fingerprints and topological structure descriptors.

ProfhEX: AI-based platform for small molecules liability profiling

Off-target drug interactions are a major reason for candidate failure in the drug discovery process. Anticipating potential drug's adverse effects in the early stages is necessary to minimize health risks to patients, animal testing, and economical costs. With the constantly increasing size of virtual screening libraries, AI-driven methods can be exploited as first-tier screening tools to provide liability estimation for drug candidates. In this work we present ProfhEX, an AI-driven suite of 46 OECD-compliant machine learning models that can profile small molecules on 7 relevant liability groups: cardiovascular, central nervous system, gastrointestinal, endocrine, renal, pulmonary and immune system toxicities. Experimental affinity data was collected from public and commercial data sources. The entire chemical space comprised 289'202 activity data for a total of 210'116 unique compounds, spanning over 46 targets with dataset sizes ranging from 819 to 18896. Gradient boosting and random forest algorithms were initially employed and ensembled for the selection of a champion model. Models were validated according to the OECD principles, including robust internal (cross validation, bootstrap, y-scrambling) and external validation. Champion models achieved an average Pearson correlation coefficient of 0.84 (SD of 0.05), an R² determination coefficient of 0.68 (SD = 0.1) and a root mean squared error of 0.69 (SD of 0.08). All liability groups showed good hit-detection power with an average enrichment factor at 5% of 13.1 (SD of 4.5) and AUC of 0.92 (SD of 0.05). Benchmarking against already existing tools demonstrated the predictive power of ProfhEX models for large-scale liability profiling. This platform will be further expanded with the inclusion of new targets and through complementary modelling approaches, such as structure and pharmacophore-based models. ProfhEX is freely accessible at the following address: https://profhex.exscalate.eu/ .

A QSAR Study for Antileishmanial 2-Phenyl-2,3-dihydrobenzofurans †

Leishmaniasis, a parasitic disease that represents a threat to the life of millions of people around the globe, is currently lacking effective treatments. We have previously reported on the antileishmanial activity of a series of synthetic 2-phenyl-2,3-dihydrobenzofurans and some qualitative structure-activity relationships within this set of neolignan analogues. Therefore, in the present study, various quantitative structure-activity relationship (QSAR) models were created to explain and predict the antileishmanial activity of these compounds. Comparing the performance of QSAR models based on molecular descriptors and multiple linear regression, random forest, and support vector regression with models based on 3D molecular structures and their interaction fields (MIFs) with partial least squares regression, it turned out that the latter (i.e., 3D-QSAR models) were clearly superior to the former. MIF analysis for the best-performing and statistically most robust 3D-QSAR model revealed the most important structural features required for antileishmanial activity. Thus, this model can guide decision-making during further development by predicting the activity of potentially new leishmanicidal dihydrobenzofurans before synthesis.

Ensemble of structure and ligand-based classification models for hERG liability profiling

Drug-induced cardiotoxicity represents one of the most critical safety concerns in the early stages of drug development. The blockade of the human ether-à-go-go-related potassium channel (hERG) is the most frequent cause of cardiotoxicity, as it is associated to long QT syndrome which can lead to fatal arrhythmias. Therefore, assessing hERG liability of new drugs candidates is crucial to avoid undesired cardiotoxic effects. In this scenario, computational approaches have emerged as useful tools for the development of predictive models able to identify potential hERG blockers. In the last years, several efforts have been addressed to generate ligand-based (LB) models due to the lack of experimental structural information about hERG channel. However, these methods rely on the structural features of the molecules used to generate the model and often fail in correctly predicting new chemical scaffolds. Recently, the 3D structure of hERG channel has been experimentally solved enabling the use of structure-based (SB) strategies which may overcome the limitations of the LB approaches. In this study, we compared the performances achieved by both LB and SB classifiers for hERG-related cardiotoxicity developed by using Random Forest algorithm and employing a training set containing 12789 hERG binders. The SB models were trained on a set of scoring functions computed by docking and rescoring calculations, while the LB classifiers were built on a set of physicochemical descriptors and fingerprints. Furthermore, models combining the LB and SB features were developed as well. All the generated models were internally validated by ten-fold cross-validation on the TS and further verified on an external test set. The former revealed that the best performance was achieved by the LB model, while the model combining the LB and the SB attributes displayed the best results when applied on the external test set highlighting the usefulness of the integration of LB and SB features in correctly predicting unseen molecules. Overall, our predictive models showed satisfactory performances providing new useful tools to filter out potential cardiotoxic drug candidates in the early phase of drug discovery.

Advances in Ultra-High-Resolution Mass Spectrometry for Pharmaceutical Analysis

Pharmaceutical analysis refers to an area of analytical chemistry that deals with active compounds either by themselves (drug substance) or when formulated with excipients (drug product). In a less simplistic way, it can be defined as a complex science involving various disciplines, e.g., drug development, pharmacokinetics, drug metabolism, tissue distribution studies, and environmental contamination analyses. As such, the pharmaceutical analysis covers drug development to its impact on health and the environment. Moreover, due to the need for safe and effective medications, the pharmaceutical industry is one of the most heavily regulated sectors of the global economy. For this reason, powerful analytical instrumentation and efficient methods are required. In the last decades, mass spectrometry has been increasingly used in pharmaceutical analysis both for research aims and routine quality controls. Among different instrumental setups, ultra-high-resolution mass spectrometry with Fourier transform instruments, i.e., Fourier transform ion cyclotron resonance (FTICR) and Orbitrap, gives access to valuable molecular information for pharmaceutical analysis. In fact, thanks to their high resolving power, mass accuracy, and dynamic range, reliable molecular formula assignments or trace analysis in complex mixtures can be obtained. This review summarizes the principles of the two main types of Fourier transform mass spectrometers, and it highlights applications, developments, and future perspectives in pharmaceutical analysis.

Identification of Putative Drug Targets in Highly Resistant Gram-Negative Bacteria; and Drug Discovery Against Glycyl-tRNA Synthetase as a New Target

Background

Gram-negative bacterial infections are on the rise due to the high prevalence of multidrug-resistant bacteria, and efforts must be made to identify novel drug targets and then new antibiotics.

Methods

In the upstream part, we retrieved the genome sequences of 4 highly resistant Gram-negative bacteria (e.g., Acinetobacter baumannii, Klebsiella pneumoniae, Pseudomonas aeruginosa, and Enterobacter cloacae). The core proteins were assessed to find common, cytoplasmic, and essential proteins with no similarity to the human proteome. Novel drug targets were identified using DrugBank, and their sequence conservancy was evaluated. Protein Data Bank files and STRING interaction networks were assessed. Finally, the aminoacylation cavity of glycyl-tRNA synthetase (GlyQ) was virtually screened to identify novel inhibitors using AutoDock Vina and the StreptomeDB library. Ligands with high binding affinity were clustered, and then the pharmacokinetics properties of therapeutic agents were investigated.

Results

A total of 6 common proteins (e.g., RP-L28, RP-L30, RP-S20, RP-S21, Rnt, and GlyQ) were selected as novel and widespread drug targets against highly resistant Gram-negative superbugs based on different criteria. In the downstream analysis, virtual screening revealed that Rimocidin, Flavofungin, Chaxamycin, 11,11'-O-dimethyl-14'-deethyl-14'-methylelaiophylin, and Platensimycin were promising hit compounds against GlyQ protein. Finally, 11,11'-O-dimethyl-14'-deethyl-14'-methylelaiophylin was identified as the best potential inhibitor of GlyQ protein. This compound showed high absorption capacity in the human intestine.

Conclusion

The results of this study provide 6 common putative new drug targets against 4 highly resistant and Gram-negative bacteria. Moreover, we presented 5 different hit compounds against GlyQ protein as a novel therapeutic target. However, further in vitro and in vivo studies are needed to explore the bactericidal effects of proposed hit compounds against these superbugs.

Integrating structure annotation and machine learning approaches to develop graphene toxicity models

Modern nanotechnology provides efficient and cost-effective nanomaterials (NMs). The increasing usage of NMs arises great concerns regarding nanotoxicity in humans. Traditional animal testing of nanotoxicity is expensive and time-consuming. Modeling studies using machine learning (ML) approaches are promising alternatives to direct evaluation of nanotoxicity based on nanostructure features. However, NMs, including two-dimensional nanomaterials (2DNMs) such as graphenes, have complex structures making them difficult to annotate and quantify the nanostructures for modeling purposes. To address this issue, we constructed a virtual graphenes library using nanostructure annotation techniques. The irregular graphene structures were generated by modifying virtual nanosheets. The nanostructures were digitalized from the annotated graphenes. Based on the annotated nanostructures, geometrical nanodescriptors were computed using Delaunay tessellation approach for ML modeling. The partial least square regression (PLSR) models for the graphenes were built and validated using a leave-one-out cross-validation (LOOCV) procedure. The resulted models showed good predictivity in four toxicity-related endpoints with the coefficient of determination (R2) ranging from 0.558 to 0.822. This study provides a novel nanostructure annotation strategy that can be applied to generate high-quality nanodescriptors for ML model developments, which can be widely applied to nanoinformatics studies of graphenes and other NMs.

A New Anticancer Semisynthetic Theobromine Derivative Targeting EGFR Protein: CADDD Study

A new lead compound has been designed as an antiangiogenic EGFR inhibitor that has the pharmacophoric characteristics to bind with the catalytic pocket of EGFR protein. The designed lead compound is a (para-chloro)acetamide derivative of the alkaloid, theobromine, (T-1-PCPA). At first, we started with deep density functional theory (DFT) calculations for T-1-PCPA to confirm and optimize its 3D structure. Additionally, the DFT studies identified the electrostatic potential, global reactive indices and total density of states expecting a high level of reactivity for T-1-PCPA. Secondly, the affinity of T-1-PCPA to bind and inhibit the EGFR protein was studied and confirmed through detailed structure-based computational studies including the molecular docking against EGFR^WT and EGFR^T790M, Molecular dynamics (MD) over 100 ns, MM-GPSA and PLIP experiments. Before the preparation, the computational ADME and toxicity profiles of T-1-PCPA have been investigated and its safety and the general drug-likeness predicted. Accordingly, T-1-PCPA was semi-synthesized to scrutinize the proposed design and the obtained in silico results. Interestingly, T-1-PCPA inhibited in vitro EGFR^WT with an IC₅₀ value of 25.35 nM, comparing that of erlotinib (5.90 nM). Additionally, T-1-PCPA inhibited the growth of A549 and HCT-116 malignant cell lines with IC₅₀ values of 31.74 and 20.40 µM, respectively, comparing erlotinib that expressed IC₅₀ values of 6.73 and 16.35 µM, respectively.

Identification of Novel Arachidonic Acid 15-Lipoxygenase Inhibitors Based on the Bayesian Classifier Model and Computer-Aided High-Throughput Virtual Screening

Ferroptosis is an iron-dependent lipid peroxidative form of cell death that is distinct from apoptosis and necrosis. ALOX15, also known as arachidonic acid 15-lipoxygenase, promotes ferroptosis by converting intracellular unsaturated lipids into oxidized lipid intermediates and is an important ferroptosis target. In this study, a naive Bayesian machine learning classifier with a structure-based, high-throughput screening approach and a molecular docking program were combined to screen for three compounds with excellent target-binding potential. In the absorption, distribution, metabolism, excretion, and toxicity characterization, three candidate molecules were predicted to exhibit drug-like properties. The subsequent molecular dynamics simulations confirmed their stable binding to the targets. The findings indicated that the compounds exhibited excellent potential ALOX15 inhibitor capacity, thereby providing novel candidates for the treatment of inflammatory ischemia-related diseases caused by ferroptosis.

Development, Validation, and Application of a Human Reproductive Toxicity Prediction Model Based on Adverse Outcome Pathway

A growing number of environmental contaminants have been proved to have reproductive toxicity to males and females. However, the unclear toxicological mechanism of reproductive toxicants limits the development of virtual screening methods. By consolidating androgen (AR)-/estrogen receptors (ERs)-mediated adverse outcome pathways (AOPs) with more than 8000 chemical substances, we uncovered relationships between chemical features, a series of pathway-related effects, and reproductive apical outcomes─changes in sex organ weights. An AOP-based computational model named RepTox was developed and evaluated to predict and characterize chemicals' reproductive toxicity for males and females. Results showed that RepTox has three outstanding advantages. (I) Compared with the traditional models (37 and 81% accuracy, respectively), AOP significantly improved the predictive robustness of RepTox (96.3% accuracy). (II) Compared with the application domain (AD) of models based on small in vivo datasets, AOP expanded the ADs of RepTox by 1.65-fold for male and 3.77-fold for female, respectively. (III) RepTox implied that hydrophobicity, cyclopentanol substructure, and several topological indices (e.g., hydrogen-bond acceptors) were important, unbiased features associated with reproductive toxicants. Finally, RepTox was applied to the inventory of existing chemical substances of China and identified 2100 and 7281 potential toxicants to the male and female reproductive systems, respectively.

Identifications of good and bad structural fragments of hydrazone/2,5-disubstituted-1,3,4-oxadiazole hybrids with correlation intensity index and consensus modelling using Monte Carlo based QSAR studies, their molecular docking and ADME analysis

The application of QSAR along with other in silico tools like molecular docking, and molecular dynamics provide a lot of promise for finding new treatments for life-threatening diseases like Type 2 diabetes mellitus (T2DM). The present study is an attempt to develop Monte Carlo algorithm-based QSAR models using freely available CORAL software. The experimental data on the α-amylase inhibition by a series of benzothiazole-linked hydrazone/2,5-disubstituted-1,3,4-oxadiazole hybrids were selected as endpoint for the model generation. Initially, a total of eight QSAR models were built using correlation intensity index (CII) as a criterion of predictive potential. The model developed from split 6 using CII was the most reliable because of the highest numerical value of the determination coefficient of the validation set (r²_VAL = 0.8739). The important structural fragments responsible for altering the endpoint were also extracted from the best-built model. With the goal of improved prediction quality and lower prediction errors, the validated models were used to build consensus models. Molecular docking was used to know the binding mode and pose of the selected derivatives. Further, to get insight into their metabolism by living beings, ADME studies were investigated using internet freeware, SwissADME.

Modern drug discovery applications for the identification of novel candidates for COVID-19 infections

In early December 2019, a large pneumonia epidemic occurred in Wuhan, China. The World Health Organization is concerned about the outbreak of another coronavirus with the powerful, rapid, and contagious transmission. Anyone with minor symptoms like fever and cough or travel history to contaminated places might be suspected of having COVID-19. COVID-19 therapy focuses on treating the disease's symptoms. So far, no such therapeutic molecule has been shown effective in treating this condition. So the treatment is mostly supportive and plasma. Globally, numerous studies and researchers have recently started fighting this virus. Vaccines and chemical compounds are also being investigated against infection. COVID-19 was successfully diagnosed using RNA detection and very sensitive RT-PCR (reverse transcription-polymerase chain reaction). The evolution of particular vaccinations is required to reduce illness severity and spread. Numerous computational analyses and molecular docking have predicted various target compounds that might stop this condition. This paper examines the main characteristics of coronavirus and the computational analyses necessary to avoid infection.

Drug Discovery by Automated Adaptation of Chemical Structure and Identity

Computer-aided drug design offers the potential to dramatically reduce the cost and effort required for drug discovery. While screening-based methods are valuable in the early stages of hit identification, they are frequently succeeded by iterative, hypothesis-driven computations that require recurrent investment of human time and intuition. To increase automation, we introduce a computational method for lead refinement that combines concerted dynamics of the ligand/protein complex via molecular dynamics simulations with integrated Monte Carlo-based changes in the chemical formula of the ligand. This approach, which we refer to as ligand-exchange Monte Carlo molecular dynamics, accounts for solvent- and entropy-based contributions to competitive binding free energies by coupling the energetics of bound and unbound states during the ligand-exchange attempt. Quantitative comparison of relative binding free energies to reference values from free energy perturbation, conducted in vacuum, indicates that ligand-exchange Monte Carlo molecular dynamics simulations sample relevant conformational ensembles and are capable of identifying strongly binding compounds. Additional simulations demonstrate the use of an implicit solvent model. We speculate that the use of chemical graphs in which exchanges are only permitted between ligands with sufficient similarity may enable an automated search to capture some of the benefits provided by human intuition during hypothesis-guided lead refinement.

Artificial intelligence and machine-learning approaches in structure and ligand-based discovery of drugs affecting central nervous system

CNS disorders are indications with a very high unmet medical needs, relatively smaller number of available drugs, and a subpar satisfaction level among patients and caregiver. Discovery of CNS drugs is extremely expensive affair with its own unique challenges leading to extremely high attrition rates and low efficiency. With explosion of data in information age, there is hardly any aspect of life that has not been touched by data driven technologies such as artificial intelligence (AI) and machine learning (ML). Drug discovery is no exception, emergence of big data via genomic, proteomic, biological, and chemical technologies has driven pharmaceutical giants to collaborate with AI oriented companies to revolutionise drug discovery, with the goal of increasing the efficiency of the process. In recent years many examples of innovative applications of AI and ML techniques in CNS drug discovery has been reported. Research on therapeutics for diseases such as schizophrenia, Alzheimer's and Parkinsonism has been provided with a new direction and thrust from these developments. AI and ML has been applied to both ligand-based and structure-based drug discovery and design of CNS therapeutics. In this review, we have summarised the general aspects of AI and ML from the perspective of drug discovery followed by a comprehensive coverage of the recent developments in the applications of AI/ML techniques in CNS drug discovery.

Ligand fishing based on tubular microchannel modified with monoamine oxidase B for screening of the enzyme's inhibitors from Crocus sativus and Edgeworthia gardneri

A novel strategy of performing ligand fishing with enzyme-modified open tubular microchannel was proposed for screening bioactive components present in medicinal plants. Monoamine oxidase B was immobilized onto the surface of the microchannel for the first time to specifically extract its ligands when the plant's extracts solution flows through the channel. The thermal and the storage stability of immobilized monoamine oxidase B were significantly enhanced after immobilization. Crocin I and Ⅱ were extracted from Crocus sativus, and tiliroside was extracted from Edgeworthia gardneri. All the three compounds were inhibitors of the enzyme with the half-maximal inhibitory concentration values of 26.70 ± 0.91, 19.88 ± 2.78, and 15.65 ± 0.85 μM, respectively. The enzyme inhibition kinetics and molecular docking were investigated. This is the first report on the inhibitory effects of tiliroside and crocin Ⅱ. The novel ligand fishing method proposed in this work possesses advantages of rapidness, high efficiency, and tiny sample consumption compared to routine ligand fishing, with promising potential for screening active natural products in complex mixtures.