An Overview of Scoring Functions Used for Protein-Ligand Interactions in Molecular Docking

Discovery of petroleum ether extract of eclipta targeting p53/Fas pathway for the treatment of chemotherapy-induced alopecia: Network pharmacology and experimental validation

ETHNOPHARMACOLOGICAL RELEVANCE

Ecliptea herba, a traditional Chinese herbal medicine for hair loss, was first recorded in the Tang Dynasty's 'Qian Jin Yue Ling', of which the active ingredients and mechanisms of action in the treatment of chemotherapy-induced hair loss remain poorly investigated.

AIM OF THE STUDY

To investigate the effects of the petroleum ether extract of Eclipta (PEE) on alopecia and follicle damage and elucidate its potential therapeutic mechanisms using the integration of network pharmacology, bioinformatics, and experimental validation.

MATERIALS AND METHODS

UPLC-MS was used to analyse the chemical composition of PEE. A network pharmacology approach was employed to establish the 'components-targets-pathways' network of PEE to explore potential therapeutic pathways and targets. Molecular docking was used for validation, and the mechanism of PEE in treating chemotherapy-induced alopecia (CIA) was elucidated using in vitro and in vivo on CIA models.

RESULTS

UPLC-MS analysis of PEE revealed 185 components, while network pharmacology and molecular docking analyses revealed potential active compounds and their target molecules, suggesting the involvement of core genes, such as TP53, ESR1, AKT1, IL6, TNF, and EGFR. The key components included wedelolactone, dimethyl-wedelolactone, luteoloside, linarin, and hispidulin. In vivo, PEE promoted hair growth, restored the number of hair follicles, and reduced follicle apoptosis. Conversely, in vitro, PEE enhanced cell viability, reduced apoptosis, and protected HaCaT cells from damage induced by 4-hydroperoxycyclophosphamide (4-HC).

CONCLUSIONS

PEE alleviated hair follicle damage in CIA mice by inhibiting the P53/Fas pathway, which may be associated with inhibiting hair follicle cell apoptosis. This study provides a novel therapeutic strategy for treating cyclophosphamide-induced hair loss.

Structure-based drug design for TSPO: Challenges and opportunities

The translocator protein 18 kDa (TSPO) is an evolutionarily conserved mitochondrial transmembrane protein implicated in various neuropathologies and inflammatory conditions, making it a longstanding diagnostic and therapeutic target of interest. Despite the development of various classes of TSPO ligand chemotypes, and the elucidation of bacterial and non-human mammalian experimental structures, many unknowns exist surrounding its differential structural and functional features in health and disease. There are several limitations associated with currently used computational methodologies for modelling the native structure and ligand-binding behaviour of this enigmatic protein. In this perspective, we provide a critical analysis of the developments in the uses of these methods, outlining their uses, inherent limitations, and continuing challenges. We offer suggestions of unexplored opportunities that exist in the use of computational methodologies which offer promise for enhancing our understanding of the TSPO.

FitScore: a fast machine learning-based score for 3D virtual screening enrichment

Enhancing virtual screening enrichment has become an urgent problem in computational chemistry, driven by increasingly large databases of commercially available compounds, without a commensurate drop in in vitro screening costs. Docking these large databases is possible with cloud-scale computing. However, rapid docking necessitates compromises in scoring, often leading to poor enrichment and an abundance of false positives in docking results. This work describes a new scoring function composed of two parts - a knowledge-based component that predicts the probability of a particular atom type being in a particular receptor environment, and a tunable weight matrix that converts the probability predictions into a dimensionless score suitable for virtual screening enrichment. This score, the FitScore, represents the compatibility between the ligand and the binding site and is capable of a high degree of enrichment across standardized docking test sets.

Fully Flexible Molecular Alignment Enables Accurate Ligand Structure Modeling

Accurate protein-ligand binding poses are the prerequisites of structure-based binding affinity prediction and provide the structural basis for in-depth lead optimization in small molecule drug design. However, it is challenging to provide reasonable predictions of binding poses for different molecules due to the complexity and diversity of the chemical space of small molecules. Similarity-based molecular alignment techniques can effectively narrow the search range, as structurally similar molecules are likely to have similar binding modes, with higher similarity usually correlated to higher success rates. However, molecular similarity is not consistently high because molecules often require changes to achieve specific purposes, leading to reduced alignment precision. To address this issue, we propose a new alignment method─Z-align. This method uses topological structural information as a criterion for evaluating similarity, reducing the reliance on molecular fingerprint similarity. Our method has achieved success rates significantly higher than those of other methods at moderate levels of similarity. Additionally, our approach can comprehensively and flexibly optimize bond lengths and angles of molecules, maintaining a high accuracy even when dealing with larger molecules. Consequently, our proposed solution helps in achieving more accurate binding poses in protein-ligand docking problems, facilitating the development of small molecule drugs. Z-align is freely available as a web server at https://cloud.zelixir.com/zalign/home.

Building shape-focused pharmacophore models for effective docking screening

The performance of molecular docking can be improved by comparing the shape similarity of the flexibly sampled poses against the target proteins' inverted binding cavities. The effectiveness of these pseudo-ligands or negative image-based models in docking rescoring is boosted further by performing enrichment-driven optimization. Here, we introduce a novel shape-focused pharmacophore modeling algorithm O-LAP that generates a new class of cavity-filling models by clumping together overlapping atomic content via pairwise distance graph clustering. Top-ranked poses of flexibly docked active ligands were used as the modeling input and multiple alternative clustering settings were benchmark-tested thoroughly with five demanding drug targets using random training/test divisions. In docking rescoring, the O-LAP modeling typically improved massively on the default docking enrichment; furthermore, the results indicate that the clustered models work well in rigid docking. The C+ +/Qt5-based algorithm O-LAP is released under the GNU General Public License v3.0 via GitHub ( https://github.com/jvlehtonen/overlap-toolkit ). SCIENTIFIC CONTRIBUTION: This study introduces O-LAP, a C++/Qt5-based graph clustering software for generating new type of shape-focused pharmacophore models. In the O-LAP modeling, the target protein cavity is filled with flexibly docked active ligands, the overlapping ligand atoms are clustered, and the shape/electrostatic potential of the resulting model is compared against the flexibly sampled molecular docking poses. The O-LAP modeling is shown to ensure high enrichment in both docking rescoring and rigid docking based on comprehensive benchmark-testing.

Identification of potent indolizine derivatives against Mycobacterial tuberculosis: In vitro anti-TB properties, in silico target validation, molecular docking and dynamics studies

In the current study, two sets of compounds: (E)-1-(2-(4-substitutedphenyl)-2-oxoethyl)-4-((hydroxyimino)methyl)pyridinium derivatives (3a-3e); and (E)-3-(substitutedbenzoyl)-7-((hydroxyimino)methyl)-2-substitutedindolizine-1-carboxylate derivatives (5a-5j), were synthesized and biologically evaluated against two strains of Mycobacterial tuberculosis (ATCC 25177) and multi-drug resistant (MDR) strains. Further, they were also tested in vitro against the mycobacterial InhA enzyme. The in vitro results showed excellent inhibitory activities against both MTB strains and compounds 5a-5j were found to be more potent, and their MIC values ranged from 5 to 16 μg/mL and 16-64 μg/mL against the M. tuberculosis (ATCC 25177) and MDR-TB strains, respectively. Compound 5h with phenyl and 4-fluorobenzoyl groups attached to the 2- and 3-position of the indolizine core was found to be the most active against both strains with MIC values of 5 μg/mL and 16 μg/mL, respectively. On the other hand, the two sets of compounds showed weak to moderate inhibition of InhA enzyme activity that ranged from 5 to 17 % and 10-52 %, respectively, with compound 5f containing 4-fluoro benzoyl group attached to the 3-position of the indolizine core being the most active (52 % inhibition of InhA). Unfortunately, there was no clear correlation between the InhA inhibitory activity and MIC values of the tested compounds, indicating the probability that they might have different modes of action other than InhA inhibition. Therefore, a computational investigation was conducted by employing molecular docking to identify their putative drug target(s) and, consequently, understand their mechanism of action. A panel of 20 essential mycobacterial enzymes was investigated, of which β-ketoacyl acyl carrier protein synthase I (KasA) and pyridoxal-5'-phosphate (PLP)-dependent aminotransferase (BioA) enzymes were revealed as putative targets for compounds 3a-3e and 5a-5j, respectively. Moreover, in silico ADMET predictions showed adequate properties for these compounds, making them promising leads worthy of further optimization.

Spautin-1 promotes PINK1-PRKN-dependent mitophagy and improves associative learning capability in an alzheimer disease animal model

Spautin-1 is a well-known macroautophagy/autophagy inhibitor via suppressing the deubiquitinases USP10 and USP13 and promoting the degradation of the PIK3C3/VPS34-BECN1 complex, while its effect on selective autophagy remains poorly understood. Mitophagy is a selective form of autophagy for removal of damaged and superfluous mitochondria via the autophagy-lysosome pathway. Here, we report a surprising discovery that, while spautin-1 remains as an effective autophagy inhibitor, it promotes PINK1-PRKN-dependent mitophagy induced by mitochondrial damage agents. Mechanistically, spautin-1 facilitates the stabilization and activation of the full-length PINK1 at the outer mitochondrial membrane (OMM) via binding to components of the TOMM complex (TOMM70 and TOMM20), leading to the disruption of the mitochondrial import of PINK1 and prevention of PARL-mediated PINK1 cleavage. Moreover, spautin-1 induces neuronal mitophagy in Caenorhabditis elegans (C. elegans) in a PINK-1-PDR-1-dependent manner. Functionally, spautin-1 is capable of improving associative learning capability in an Alzheimer disease (AD) C. elegans model. In summary, we report a novel function of spautin-1 in promoting mitophagy via the PINK1-PRKN pathway. As deficiency of mitophagy is closely implicated in the pathogenesis of neurodegenerative disorders, the pro-mitophagy function of spautin-1 might suggest its therapeutic potential in neurodegenerative disorders such as AD.Abbreviations: AD, Alzheimer disease; ATG, autophagy related; BafA1, bafilomycin A1; CALCOCO2/NDP52, calcium binding and coiled-coil domain 2; CCCP, carbonyl cyanide m-chlorophenyl hydrazone; COX4/COX IV, cytochrome c oxidase subunit 4; EBSS, Earle's balanced salt; ECAR, extracellular acidification rate; GFP, green fluorescent protein; IA, isoamyl alcohol; IMM, inner mitochondrial membrane; MAP1LC3/LC3, microtubule associated protein 1 light chain 3; MMP, mitochondrial membrane potential; mtDNA, mitochondrial DNA; nDNA, nuclear DNA; O/A, oligomycin-antimycin; OCR, oxygen consumption rate; OMM, outer mitochondrial membrane; OPTN, optineurin; PARL, presenilin associated rhomboid like; PINK1, PTEN induced kinase 1; PRKN, parkin RBR E3 ubiquitin protein ligase; p-Ser65-Ub, phosphorylation of Ub at Ser65; TIMM23, translocase of inner mitochondrial membrane 23; TOMM, translocase of outer mitochondrial membrane; USP10, ubiquitin specific peptidase 10; USP13, ubiquitin specific peptidase 13; VAL, valinomycin; YFP, yellow fluorescent protein.

Quantum Molecular Docking with a Quantum-Inspired Algorithm

Molecular docking (MD) is a crucial task in drug design, which predicts the position, orientation, and conformation of the ligand when it is bound to a target protein. It can be interpreted as a combinatorial optimization problem, where quantum annealing (QA) has shown a promising advantage for solving combinatorial optimization. In this work, we propose a novel quantum molecular docking (QMD) approach based on a QA-inspired algorithm. We construct two binary encoding methods to efficiently discretize the degrees of freedom with an exponentially reduced number of bits and propose a smoothing filter to rescale the rugged objective function. We propose a new quantum-inspired algorithm, hopscotch simulated bifurcation (hSB), showing great advantages in optimizing over extremely rugged energy landscapes. This hSB can be applied to any formulation of an objective function under binary variables. An adaptive local continuous search is also introduced for further optimization of the discretized solution from hSB. Concerning the stability of docking, we propose a perturbation detection method to help rank the candidate poses. We demonstrate our approach on a typical data set. QMD has shown advantages over the search-based Autodock Vina and the deep-learning DIFFDOCK in both redocking and self-docking scenarios. These results indicate that quantum-inspired algorithms can be applied to solve practical problems in drug discovery even before quantum hardware become mature.

Epigenetic target identification strategy based on multi-feature learning

The identification of potential epigenetic targets for a known bioactive compound is essential and promising as more and more epigenetic drugs are used in cancer clinical treatment and the availability of chemogenomic data related to epigenetics increases. In this study, we introduce a novel epigenetic target identification strategy (ETI-Strategy) that integrates a multi-task graph convolutional neural network prior model and a protein-ligand interaction classification discriminating model using large-scale bioactivity data for a panel of 55 epigenetic targets. Our approach utilizes machine learning techniques to achieve an AUC value of 0.934 for the prior model and 0.830 for the discriminating model, outperforming inverse docking in predicting protein-ligand interactions. When comparing with other open-source target identification tools, it was found that only our tool was able to accurately predict all the targets corresponding to each compound. This further demonstrates the ability of our strategy to take full advantage of molecular-level information as well as protein-level information in molecular activity prediction. Our work highlights the contribution of machine learning in the identification of potential epigenetic targets and offers a novel approach for epigenetic drug discovery and development.Communicated by Ramaswamy H. Sarma.

Binding Affinity Determination in Drug Design: Insights from Lock and Key, Induced Fit, Conformational Selection, and Inhibitor Trapping Models

Binding affinity is a fundamental parameter in drug design, describing the strength of the interaction between a molecule and its target protein. Accurately predicting binding affinity is crucial for the rapid development of novel therapeutics, the prioritization of promising candidates, and the optimization of their properties through rational design strategies. Binding affinity is determined by the mechanism of recognition between proteins and ligands. Various models, including the lock and key, induced fit, and conformational selection, have been proposed to explain this recognition process. However, current computational strategies to predict binding affinity, which are based on these models, have yet to produce satisfactory results. This article explores the connection between binding affinity and these protein-ligand interaction models, highlighting that they offer an incomplete picture of the mechanism governing binding affinity. Specifically, current models primarily center on the binding of the ligand and do not address its dissociation. In this context, the concept of ligand trapping is introduced, which models the mechanisms of dissociation. When combined with the current models, this concept can provide a unified theoretical framework that may allow for the accurate determination of the ligands' binding affinity.

Structural and functional analysis of Cyanovirin-N homologs: Carbohydrate binding affinities and antiviral potential of cyanobacterial peptides

Cyanobacteria, a group of photosynthetic prokaryotes, can sinthesize several substances due to their secondary metabolism, with notable properties, such as Cyanovirin-N(CVN), a carbohydrate-binding lectin, that exhibits antiviral activity against several pathogens, due to its ability to bind viral surface carbohydrates such as mannose, thus interfering with the viral entry on the cell. CVN has been described in several cyanobacterial strains and shows biotechnological potential for the development of drugs of pharmaceutical interest. This study focuses on the genomic exploration and characterization of Cyanovirin-N homologs to assess the conservation of carbohydrate-binding affinity within the group. The analysis of their antiviral properties was carried out using bioinformatics tools to study protein models through an in silico pipeline, following the steps of genomic prospection on public databases, homology modeling, docking, molecular dynamics and energetic analysis. Mannose served as the reference ligand, and the lectins' binding affinity with mannose was assessed across Cyanovirin-N homologs. Genomic mining identified 33 cyanobacterial lectin sequences, which underwent structural and functional characterization. The results obtained from this work indicate strong carbohydrate affinity on several homologs, pointing to the conservation of antiviral properties alongside the group. However, this affinity was not uniformly distributed among sequences, exhibiting significant heterogeneity in binding site residues, suggesting potential multi-ligand binding capabilities on the Cyanovirin-N homologs group. Studies focused on the properties involved in these molecules and the investigation of the genetic diversity of Cyanovirin-N homologs could provide valuable insights into the discovery of new drug candidates, harvesting the potential of bioinformatics for large-scale functional and structural analysis.

Na+/K+-ATPase: More than an Electrogenic Pump

The sodium pump, or Na+/K+-ATPase (NKA), is an essential enzyme found in the plasma membrane of all animal cells. Its primary role is to transport sodium (Na+) and potassium (K+) ions across the cell membrane, using energy from ATP hydrolysis. This transport creates and maintains an electrochemical gradient, which is crucial for various cellular processes, including cell volume regulation, electrical excitability, and secondary active transport. Although the role of NKA as a pump was discovered and demonstrated several decades ago, it remains the subject of intense research. Current studies aim to delve deeper into several aspects of this molecular entity, such as describing its structure and mode of operation in atomic detail, understanding its molecular and functional diversity, and examining the consequences of its malfunction due to structural alterations. Additionally, researchers are investigating the effects of various substances that amplify or decrease its pumping activity. Beyond its role as a pump, growing evidence indicates that in various cell types, NKA also functions as a receptor for cardiac glycosides like ouabain. This receptor activity triggers the activation of various signaling pathways, producing significant morphological and physiological effects. In this report, we present the results of a comprehensive review of the most outstanding studies of the past five years. We highlight the progress made regarding this new concept of NKA and the various cardiac glycosides that influence it. Furthermore, we emphasize NKA's role in epithelial physiology, particularly its function as a receptor for cardiac glycosides that trigger intracellular signals regulating cell-cell contacts, proliferation, differentiation, and adhesion. We also analyze the role of NKA β-subunits as cell adhesion molecules in glia and epithelial cells.

Efficient Refinement of Complex Structures of Flexible Histone Peptides Using Post-Docking Molecular Dynamics Protocols

Histones are keys to many epigenetic events and their complexes have therapeutic and diagnostic importance. The determination of the structures of histone complexes is fundamental in the design of new drugs. Computational molecular docking is widely used for the prediction of target-ligand complexes. Large, linear peptides like the tail regions of histones are challenging ligands for docking due to their large conformational flexibility, extensive hydration, and weak interactions with the shallow binding pockets of their reader proteins. Thus, fast docking methods often fail to produce complex structures of such peptide ligands at a level appropriate for drug design. To address this challenge, and improve the structural quality of the docked complexes, post-docking refinement has been applied using various molecular dynamics (MD) approaches. However, a final consensus has not been reached on the desired MD refinement protocol. In this present study, MD refinement strategies were systematically explored on a set of problematic complexes of histone peptide ligands with relatively large errors in their docked geometries. Six protocols were compared that differ in their MD simulation parameters. In all cases, pre-MD hydration of the complex interface regions was applied to avoid the unwanted presence of empty cavities. The best-performing protocol achieved a median of 32% improvement over the docked structures in terms of the change in root mean squared deviations from the experimental references. The influence of structural factors and explicit hydration on the performance of post-docking MD refinements are also discussed to help with their implementation in future methods and applications.

Rational Design of Drugs Targeting G-Protein-Coupled Receptors: Ligand Search and Screening

G protein-coupled receptors (GPCRs) are transmembrane proteins that participate in many physiological processes and represent major pharmacological targets. Recent advances in structural biology of GPCRs have enabled the development of drugs based on the receptor structure (structure-based drug design, SBDD). SBDD utilizes information about the receptor-ligand complex to search for suitable compounds, thus expanding the chemical space of possible receptor ligands without the need for experimental screening. The review describes the use of structure-based virtual screening (SBVS) for GPCR ligands and approaches for the functional testing of potential drug compounds, as well as discusses recent advances and successful examples in the application of SBDD for the identification of GPCR ligands.

Deep Generative Models in De Novo Drug Molecule Generation

The discovery of new drugs has important implications for human health. Traditional methods for drug discovery rely on experiments to optimize the structure of lead molecules, which are time-consuming and high-cost. Recently, artificial intelligence has exhibited promising and efficient performance for drug-like molecule generation. In particular, deep generative models achieve great success in de novo generation of drug-like molecules with desired properties, showing massive potential for novel drug discovery. In this study, we review the recent progress of molecule generation using deep generative models, mainly focusing on molecule representations, public databases, data processing tools, and advanced artificial intelligence based molecule generation frameworks. In particular, we present a comprehensive comparison of state-of-the-art deep generative models for molecule generation and a summary of commonly used molecular design strategies. We identify research gaps and challenges of molecule generation such as the need for better databases, missing 3D information in molecular representation, and the lack of high-precision evaluation metrics. We suggest future directions for molecular generation and drug discovery.

GEFormerDTA: drug target affinity prediction based on transformer graph for early fusion

Predicting the interaction affinity between drugs and target proteins is crucial for rapid and accurate drug discovery and repositioning. Therefore, more accurate prediction of DTA has become a key area of research in the field of drug discovery and drug repositioning. However, traditional experimental methods have disadvantages such as long operation cycles, high manpower requirements, and high economic costs, making it difficult to predict specific interactions between drugs and target proteins quickly and accurately. Some methods mainly use the SMILES sequence of drugs and the primary structure of proteins as inputs, ignoring the graph information such as bond encoding, degree centrality encoding, spatial encoding of drug molecule graphs, and the structural information of proteins such as secondary structure and accessible surface area. Moreover, previous methods were based on protein sequences to learn feature representations, neglecting the completeness of information. To address the completeness of drug and protein structure information, we propose a Transformer graph-based early fusion research approach for drug-target affinity prediction (GEFormerDTA). Our method reduces prediction errors caused by insufficient feature learning. Experimental results on Davis and KIBA datasets showed a better prediction of drugtarget affinity than existing affinity prediction methods.

A new paradigm for applying deep learning to protein-ligand interaction prediction

Protein-ligand interaction prediction presents a significant challenge in drug design. Numerous machine learning and deep learning (DL) models have been developed to accurately identify docking poses of ligands and active compounds against specific targets. However, current models often suffer from inadequate accuracy or lack practical physical significance in their scoring systems. In this research paper, we introduce IGModel, a novel approach that utilizes the geometric information of protein-ligand complexes as input for predicting the root mean square deviation of docking poses and the binding strength (pKd, the negative value of the logarithm of binding affinity) within the same prediction framework. This ensures that the output scores carry intuitive meaning. We extensively evaluate the performance of IGModel on various docking power test sets, including the CASF-2016 benchmark, PDBbind-CrossDocked-Core and DISCO set, consistently achieving state-of-the-art accuracies. Furthermore, we assess IGModel's generalizability and robustness by evaluating it on unbiased test sets and sets containing target structures generated by AlphaFold2. The exceptional performance of IGModel on these sets demonstrates its efficacy. Additionally, we visualize the latent space of protein-ligand interactions encoded by IGModel and conduct interpretability analysis, providing valuable insights. This study presents a novel framework for DL-based prediction of protein-ligand interactions, contributing to the advancement of this field. The IGModel is available at GitHub repository https://github.com/zchwang/IGModel.

Benchmarking ANI potentials as a rescoring function and screening FDA drugs for SARS-CoV-2 Mpro

Here, we introduce the use of ANI-ML potentials as a rescoring function in the host-guest interaction in molecular docking. Our results show that the "docking power" of ANI potentials can compete with the current scoring functions at the same level of computational cost. Benchmarking studies on CASF-2016 dataset showed that ANI is ranked in the top 5 scoring functions among the other 34 tested. In particular, the ANI predicted interaction energies when used in conjunction with GOLD-PLP scoring function can boost the top ranked solution to be the closest to the x-ray structure. Rapid and accurate calculation of interaction energies between ligand and protein also enables screening of millions of drug candidates/docking poses. Using a unique protocol in which docking by GOLD-PLP, rescoring by ANI-ML potentials and extensive MD simulations along with end state free energy methods are combined, we have screened FDA approved drugs against the SARS-CoV-2 main protease (Mpro). The top six drug molecules suggested by the consensus of these free energy methods have already been in clinical trials or proposed as potential drug molecules in previous theoretical and experimental studies, approving the validity and the power of accuracy in our screening method.

Enhancing Generalizability in Protein-Ligand Binding Affinity Prediction with Multimodal Contrastive Learning

Improving the generalization ability of scoring functions remains a major challenge in protein-ligand binding affinity prediction. Many machine learning methods are limited by their reliance on single-modal representations, hindering a comprehensive understanding of protein-ligand interactions. We introduce a graph-neural-network-based scoring function that utilizes a triplet contrastive learning loss to improve protein-ligand representations. In this model, three-dimensional complex representations and the fusion of two-dimensional ligand and coarse-grained pocket representations converge while distancing from decoy representations in latent space. After rigorous validation on multiple external data sets, our model exhibits commendable generalization capabilities compared to those of other deep learning-based scoring functions, marking it as a promising tool in the realm of drug discovery. In the future, our training framework can be extended to other biophysical- and biochemical-related problems such as protein-protein interaction and protein mutation prediction.

Large-Scale Pretraining Improves Sample Efficiency of Active Learning-Based Virtual Screening

Virtual screening of large compound libraries to identify potential hit candidates is one of the earliest steps in drug discovery. As the size of commercially available compound collections grows exponentially to the scale of billions, active learning and Bayesian optimization have recently been proven as effective methods of narrowing down the search space. An essential component of those methods is a surrogate machine learning model that predicts the desired properties of compounds. An accurate model can achieve high sample efficiency by finding hits with only a fraction of the entire library being virtually screened. In this study, we examined the performance of a pretrained transformer-based language model and graph neural network in a Bayesian optimization active learning framework. The best pretrained model identifies 58.97% of the top-50,000 compounds after screening only 0.6% of an ultralarge library containing 99.5 million compounds, improving 8% over the previous state-of-the-art baseline. Through extensive benchmarks, we show that the superior performance of pretrained models persists in both structure-based and ligand-based drug discovery. Pretrained models can serve as a boost to the accuracy and sample efficiency of active learning-based virtual screening.

Spectroscopy and molecular simulation on the interaction of Nano-Kaempferol prepared by oil-in-water with two carrier proteins: An investigation of protein-protein interaction

In this work, the interaction of human serum albumin (HSA) and human holo-transferrin (HTF) with the prepared Nano-Kaempferol (Nano-KMP) through oil-in-water procedure was investigated in the form of binary and ternary systems by the utilization of different spectroscopy techniques along with molecular simulation and cancer cell experiments. According to fluorescence spectroscopy outcomes, Nano-KMP is capable of quenching both proteins as binary systems by a static mechanism, while in the form of (HSA-HTF) Nano-KMP as the ternary system, an unlinear Stern-Volmer plot was elucidated with the occurrence of both dynamic and static fluorescence quenching mechanisms in the binding interaction. In addition, the two acquired Ksv values in the ternary system signified the existence of two sets of binding sites with two different interaction behaviors. The binding constant values of HSA-Nano KMP, HTF-Nano-KMP, and (HSA-HTF) Nano-KMP complexes formation were (2.54 ± 0.03) × 104, (2.15 ± 0.02) × 104 and (1.43 ± 0.04) × 104M-1at the first set of binding sites and (4.68 ± 0.05) × 104 M-1 at the second set of binding sites, respectively. The data of thermodynamic parameters confirmed the major roles of hydrogen binding and van der Waals forces in the formation of HSA-Nano KMP and HTF-Nano KMP complexes. The thermodynamic parameter values of (HSA-HTF) Nano KMP revealed the dominance of hydrogen binding and van der Waals forces in the first set of binding sites and hydrophobic forces for the second set of binding sites. Resonance light scattering (RLS) analysis displayed the existence of a different interaction behavior for HSA-HTF complex in the presence of Nano-KMP as the ternary system. Moreover, circular dichroism (CD) technique affirmed the conformational changes of the secondary structure of proteins as binary and ternary systems. Molecular docking and molecular dynamics simulations (for 100 ns) were performed to investigate the mechanism of KMP binding to HSA, HTF, and HSA-HTF. Next to observing a concentration and time-dependent cytotoxicity, the down regulation of PI3K/AkT/mTOR pathway resulted in cell cycle arrest in SW480 cells.

Sample efficient reinforcement learning with active learning for molecular design

Reinforcement learning (RL) is a powerful and flexible paradigm for searching for solutions in high-dimensional action spaces. However, bridging the gap between playing computer games with thousands of simulated episodes and solving real scientific problems with complex and involved environments (up to actual laboratory experiments) requires improvements in terms of sample efficiency to make the most of expensive information. The discovery of new drugs is a major commercial application of RL, motivated by the very large nature of the chemical space and the need to perform multiparameter optimization (MPO) across different properties. In silico methods, such as virtual library screening (VS) and de novo molecular generation with RL, show great promise in accelerating this search. However, incorporation of increasingly complex computational models in these workflows requires increasing sample efficiency. Here, we introduce an active learning system linked with an RL model (RL-AL) for molecular design, which aims to improve the sample-efficiency of the optimization process. We identity and characterize unique challenges combining RL and AL, investigate the interplay between the systems, and develop a novel AL approach to solve the MPO problem. Our approach greatly expedites the search for novel solutions relative to baseline-RL for simple ligand- and structure-based oracle functions, with a 5-66-fold increase in hits generated for a fixed oracle budget and a 4-64-fold reduction in computational time to find a specific number of hits. Furthermore, compounds discovered through RL-AL display substantial enrichment of a multi-parameter scoring objective, indicating superior efficacy in curating high-scoring compounds, without a reduction in output diversity. This significant acceleration improves the feasibility of oracle functions that have largely been overlooked in RL due to high computational costs, for example free energy perturbation methods, and in principle is applicable to any RL domain.

In silico analysis of small molecule compounds based on pharmacophore- and backbone-leaping peroxiredoxin 1 (PRDX1) inhibitors

Abnormal expression of PRDX has been found to play a significant role in the growth of colorectal cancer and other types of tumors. Despite the identification of several PRDX1 inhibitory compounds in recent years, none of them have been utilized in clinical treatments. Therefore, we conducted a virtual screening of 210,331 small molecules from the SPECS library using PRDX1 and multiple methods. From this screening, we identified 13 compounds with the highest scores from the molecular docking analysis. To further validate the accuracy of our pharmacophore model, we constructed a structure-based pharmacophore model and analyzed the receiver operating characteristic curve (ROC curve). Through this process, we selected nine compounds using skeleton jumping and virtual screening based on the highest pharmacophore model scores. Subsequently, we examined the ADMET properties of these nine compounds to assess their drug-forming potential, resulting in three compounds with the best drug properties. Finally, we assessed the binding stability of these three candidate molecules to proteins using molecular dynamics and MM-PBSA calculations. After a comprehensive evaluation, we found that compounds 6 and 9 formed stable complexes with PRDX1 proteins and could potentially serve as competitive inhibitors of PRDX1 substrates.Communicated by Ramaswamy H. Sarma.

Curcumin in Cancer and Inflammation: An In-Depth Exploration of Molecular Interactions, Therapeutic Potentials, and the Role in Disease Management

This paper delves into the diverse and significant roles of curcumin, a polyphenolic compound from the Curcuma longa plant, in the context of cancer and inflammatory diseases. Distinguished by its unique molecular structure, curcumin exhibits potent biological activities including anti-inflammatory, antioxidant, and potential anticancer effects. The research comprehensively investigates curcumin's molecular interactions with key proteins involved in cancer progression and the inflammatory response, primarily through molecular docking studies. In cancer, curcumin's effectiveness is determined by examining its interaction with pivotal proteins like CDK2, CK2α, GSK3β, DYRK2, and EGFR, among others. These interactions suggest curcumin's potential role in impeding cancer cell proliferation and survival. Additionally, the paper highlights curcumin's impact on inflammation by examining its influence on proteins such as COX-2, CRP, PDE4, and MD-2, which are central to the inflammatory pathway. In vitro and clinical studies are extensively reviewed, shedding light on curcumin's binding mechanisms, pharmacological impacts, and therapeutic application in various cancers and inflammatory conditions. These studies are pivotal in understanding curcumin's functionality and its potential as a therapeutic agent. Conclusively, this review emphasizes the therapeutic promise of curcumin in treating a wide range of health issues, attributed to its complex chemistry and broad pharmacological properties. The research points towards curcumin's growing importance as a multi-faceted natural compound in the medical and scientific community.

Elucidating the anti-hypertensive mechanisms of Uncaria rhynchophylla-Alisma plantago-aquatica L: an integrated network pharmacology, cluster analysis, and molecular docking approach

Background: With the increasing global prevalence of hypertension, a condition that can severely affect multiple organs, there is a growing need for effective treatment options. Uncaria rhynchophylla-Alisma plantago-aquatica L. (UR-AP) is a traditional drug pair used for treating hypertension based on the liver-kidney synergy concept. However, the detailed molecular mechanisms underlying its efficacy remain unclear. Methods: This study utilized an integrative approach combining network pharmacology, cluster analysis, and molecular docking to uncover the bioactive components and targets of UR-AP in the treatment of hypertension. Initially, we extracted data from public databases to identify these components and targets. A Protein-Protein Interaction (PPI) network was constructed, followed by enrichment analysis to pinpoint the bioactive components, core targets, and pivotal pathways. Cluster analysis helped in identifying key sub-networks and hypothesizing primary targets. Furthermore, molecular docking was conducted to validate the interaction between the core targets and major bioactive components, thus confirming their potential efficacy in hypertension treatment. Results: Network pharmacological analysis identified 58 bioactive compounds in UR-AP, notably quercetin, kaempferol, beta-sitosterol (from Uncaria rhynchophylla), and Alisol B, alisol B 23-acetate (from Alisma plantago-aquatica L.), as pivotal bioactives. We pinpointed 143 targets common to both UR-AP and hypertension, highlighting MAPK1, IL6, AKT1, VEGFA, EGFR, and TP53 as central targets involved in key pathways like diastolic and endothelial function, anti-atherosclerosis, AGE-RAGE signaling, and calcium signaling. Cluster analysis emphasized IL6, TNF, AKT1, and VEGFA's roles in atherosclerosis and inflammation. Molecular docking confirmed strong interactions between these targets and UR-AP's main bioactives, underscoring their therapeutic potential. Conclusion: This research delineates UR-AP's pharmacological profile in hypertension treatment, linking traditional medicine with modern pharmacology. It highlights key bioactive components and their interactions with principal targets, suggesting UR-AP's potential as a novel therapeutic option for hypertension. The evidence from molecular docking studies supports these interactions, indicating the relevance of these components in affecting hypertension pathways. However, the study acknowledges its limitations, including the reliance on in silico analyses and the need for in vivo validation. These findings pave the way for future clinical research, aiming to integrate traditional medicine insights with contemporary scientific approaches for developing innovative hypertension therapies.

Design, Synthesis and Biological Assessment of N'-(2-Oxoindolin-3-ylidene)-6-methylimidazo[2,1-b]thiazole-5-carbohydrazides as Potential Anti-Proliferative Agents toward MCF-7 Breast Cancer

Breast cancer is a serious threat to the health and lives of women. Two novel series of N'-(2-oxoindolin-3-ylidene)-6-methylimidazo[2,1-b]thiazole-5-carbohydrazides and 1-(aryl)-3-(6-methylimidazo[2,1-b]thiazol-5-yl)ureas were designed, synthesized and investigated for their anticancer efficacy against the MCF-7 breast cell line. Three compounds of the first series showed potent activity toward MCF-7 with IC50 in the range 8.38-11.67 µM, respectively, as compared to Sorafenib (IC50 = 7.55 µM). N'-(1-butyl-2-oxoindolin-3-ylidene)-6-methylimidazo[2,1-b]thiazole-5-carbohydrazide inhibited VEGFR-2 with IC50 = 0.33 µM when compared with Sorafenib (IC50 = 0.09 µM). Furthermore, this compound was introduced to PCR assessment, where it increased Bax, caspase 8, caspase 9 and cytochrome C levels by 4.337-, 2.727-, 4.947- and 2.420-fold, respectively, while it decreased levels of Bcl-2, as the anti-apoptotic gene, by 0.359-fold when compared to the untreated control MCF-7. This compound was also arrested in the G2/M phase by 27.07%, compared with 11.31% for the control MCF-7. Furthermore, it induced early and late apoptosis in MCF-7. In addition, a molecular docking study in the VEGFR-2 active site was performed to assess the binding profile for the most active compounds. Moreover, ADME parameters of the targeted compounds were also evaluated.

Molecular docking and dynamics simulation studies uncover the host-pathogen protein-protein interactions in Penaeus vannamei and Vibrio parahaemolyticus

Shrimp aquaculture contributes significantly to global economic growth, and the whiteleg shrimp, Penaeus vannamei, is a leading species in this industry. However, Vibrio parahaemolyticus infection poses a major challenge in ensuring the success of P. vannamei aquaculture. Despite its significance in this industry, the biological knowledge of its pathogenesis remains unclear. Hence, this study was conducted to identify the interaction sites and binding affinity between several immune-related proteins of P. vannamei with V. parahaemolyticus proteins associated with virulence factors. Potential interaction sites and the binding affinity between host and pathogen proteins were identified using molecular docking and dynamics (MD) simulation. The P. vannamei-V. parahaemolyticus protein-protein interaction of Complex 1 (Ferritin-HrpE/YscL family type III secretion apparatus protein), Complex 2 (Protein kinase domain-containing protein-Chemotaxis CheY protein), and Complex 3 (GPCR-Chemotaxis CheY protein) was found to interact with -4319.76, -5271.39, and -4725.57 of the docked score and the formation of intermolecular bonds at several interacting residues. The docked scores of Complex 1, Complex 2, and Complex 3 were validated using MD simulation analysis, which revealed these complexes greatly contribute to the interactions between P. vannamei and V. parahaemolyticus proteins, with binding free energies of -22.50 kJ/mol, -30.20 kJ/mol, and -26.27 kJ/mol, respectively. This finding illustrates the capability of computational approaches to search for molecular binding sites between host and pathogen, which could increase the knowledge of Vibrio spp. infection on shrimps, which then can be used to assist in the development of effective treatment.

Targeting ion channels with ultra-large library screening for hit discovery

Ion channels play a crucial role in a variety of physiological and pathological processes, making them attractive targets for drug development in diseases such as diabetes, epilepsy, hypertension, cancer, and chronic pain. Despite the importance of ion channels in drug discovery, the vastness of chemical space and the complexity of ion channels pose significant challenges for identifying drug candidates. The use of in silico methods in drug discovery has dramatically reduced the time and cost of drug development and has the potential to revolutionize the field of medicine. Recent advances in computer hardware and software have enabled the screening of ultra-large compound libraries. Integration of different methods at various scales and dimensions is becoming an inevitable trend in drug development. In this review, we provide an overview of current state-of-the-art computational chemistry methodologies for ultra-large compound library screening and their application to ion channel drug discovery research. We discuss the advantages and limitations of various in silico techniques, including virtual screening, molecular mechanics/dynamics simulations, and machine learning-based approaches. We also highlight several successful applications of computational chemistry methodologies in ion channel drug discovery and provide insights into future directions and challenges in this field.

Sampling and Scoring in Protein-Protein Docking

Protein-protein docking is considered one of the most important techniques supporting experimental proteomics. Recent developments in the field of computer science helped to improve this computational technique so that it better handles the complexity of protein nature. Sampling algorithms are responsible for the generation of numerous protein-protein ensembles. Unfortunately, a primary docking output comprises a set of both near-native poses and decoys. Application of the efficient scoring function helps to differentiate poses with the most favorable properties from those that are very unlikely to represent a natural state of the complex. This chapter explains the importance of sampling and scoring in the process of protein-protein docking. Moreover, it summarizes advances in the field.

Exploring the role of microbial proteins in controlling environmental pollutants based on molecular simulation

Molecular simulation has been widely used to study microbial proteins' structural composition and dynamic properties, such as volatility, flexibility, and stability at the microscopic scale. Herein, this review describes the key elements of molecular docking and molecular dynamics (MD) simulations in molecular simulation; reviews the techniques combined with molecular simulation, such as crystallography, spectroscopy, molecular biology, and machine learning, to validate simulation results and bridge information gaps in the structure, microenvironmental changes, expression mechanisms, and intensity quantification; illustrates the application of molecular simulation, in characterizing the molecular mechanisms of interaction of microbial proteins with four different types of contaminants, namely heavy metals (HMs), pesticides, dyes and emerging contaminants (ECs). Finally, the review outlines the important role of molecular simulations in the study of microbial proteins for controlling environmental contamination and provides ideas for the application of molecular simulation in screening microbial proteins and incorporating targeted mutagenesis to obtain more effective contaminant control proteins.

A deep learning method for drug-target affinity prediction based on sequence interaction information mining

Background

A critical aspect of in silico drug discovery involves the prediction of drug-target affinity (DTA). Conducting wet lab experiments to determine affinity is both expensive and time-consuming, making it necessary to find alternative approaches. In recent years, deep learning has emerged as a promising technique for DTA prediction, leveraging the substantial computational power of modern computers.

Methods

We proposed a novel sequence-based approach, named KC-DTA, for predicting drug-target affinity (DTA). In this approach, we converted the target sequence into two distinct matrices, while representing the molecule compound as a graph. The proposed method utilized k-mers analysis and Cartesian product calculation to capture the interactions and evolutionary information among various residues, enabling the creation of the two matrices for target sequence. For molecule, it was represented by constructing a molecular graph where atoms serve as nodes and chemical bonds serve as edges. Subsequently, the obtained target matrices and molecule graph were utilized as inputs for convolutional neural networks (CNNs) and graph neural networks (GNNs) to extract hidden features, which were further used for the prediction of binding affinity.

Results

In order to evaluate the effectiveness of the proposed method, we conducted several experiments and made a comprehensive comparison with the state-of-the-art approaches using multiple evaluation metrics. The results of our experiments demonstrated that the KC-DTA method achieves high performance in predicting drug-target affinity (DTA). The findings of this research underscore the significance of the KC-DTA method as a valuable tool in the field of in silico drug discovery, offering promising opportunities for accelerating the drug development process. All the data and code are available for access on https://github.com/syc2017/KCDTA.

A Multi-perspective Model for Protein-Ligand-Binding Affinity Prediction

Gathering information from multi-perspective graphs is an essential issue for many applications especially for protein-ligand-binding affinity prediction. Most of traditional approaches obtained such information individually with low interpretability. In this paper, we harness the rich information from multi-perspective graphs with a general model, which abstractly represents protein-ligand complexes with better interpretability while achieving excellent predictive performance. In addition, we specially analyze the protein-ligand-binding affinity problem, taking into account the heterogeneity of proteins and ligands. Experimental evaluations demonstrate the effectiveness of our data representation strategy on public datasets by fusing information from different perspectives. All codes are available in the https://github.com/Jthy-af/HaPPy .

An overview of recent advances and challenges in predicting compound-protein interaction (CPI)

Compound-protein interactions (CPIs) are critical in drug discovery for identifying therapeutic targets, drug side effects, and repurposing existing drugs. Machine learning (ML) algorithms have emerged as powerful tools for CPI prediction, offering notable advantages in cost-effectiveness and efficiency. This review provides an overview of recent advances in both structure-based and non-structure-based CPI prediction ML models, highlighting their performance and achievements. It also offers insights into CPI prediction-related datasets and evaluation benchmarks. Lastly, the article presents a comprehensive assessment of the current landscape of CPI prediction, elucidating the challenges faced and outlining emerging trends to advance the field.

GAABind: a geometry-aware attention-based network for accurate protein-ligand binding pose and binding affinity prediction

Protein-ligand interactions are increasingly profiled at high-throughput, playing a vital role in lead compound discovery and drug optimization. Accurate prediction of binding pose and binding affinity constitutes a pivotal challenge in advancing our computational understanding of protein-ligand interactions. However, inherent limitations still exist, including high computational cost for conformational search sampling in traditional molecular docking tools, and the unsatisfactory molecular representation learning and intermolecular interaction modeling in deep learning-based methods. Here we propose a geometry-aware attention-based deep learning model, GAABind, which effectively predicts the pocket-ligand binding pose and binding affinity within a multi-task learning framework. Specifically, GAABind comprehensively captures the geometric and topological properties of both binding pockets and ligands, and employs expressive molecular representation learning to model intramolecular interactions. Moreover, GAABind proficiently learns the intermolecular many-body interactions and simulates the dynamic conformational adaptations of the ligand during its interaction with the protein through meticulously designed networks. We trained GAABind on the PDBbindv2020 and evaluated it on the CASF2016 dataset; the results indicate that GAABind achieves state-of-the-art performance in binding pose prediction and shows comparable binding affinity prediction performance. Notably, GAABind achieves a success rate of 82.8% in binding pose prediction, and the Pearson correlation between predicted and experimental binding affinities reaches up to 0.803. Additionally, we assessed GAABind's performance on the severe acute respiratory syndrome coronavirus 2 main protease cross-docking dataset. In this evaluation, GAABind demonstrates a notable success rate of 76.5% in binding pose prediction and achieves the highest Pearson correlation coefficient in binding affinity prediction compared with all baseline methods.

Integrated Molecular Modeling and Machine Learning for Drug Design

Modern therapeutic development often involves several stages that are interconnected, and multiple iterations are usually required to bring a new drug to the market. Computational approaches have increasingly become an indispensable part of helping reduce the time and cost of the research and development of new drugs. In this Perspective, we summarize our recent efforts on integrating molecular modeling and machine learning to develop computational tools for modulator design, including a pocket-guided rational design approach based on AlphaSpace to target protein-protein interactions, delta machine learning scoring functions for protein-ligand docking as well as virtual screening, and state-of-the-art deep learning models to predict calculated and experimental molecular properties based on molecular mechanics optimized geometries. Meanwhile, we discuss remaining challenges and promising directions for further development and use a retrospective example of FDA approved kinase inhibitor Erlotinib to demonstrate the use of these newly developed computational tools.

Exploring the artificial intelligence and machine learning models in the context of drug design difficulties and future potential for the pharmaceutical sectors

Artificial intelligence (AI), particularly deep learning as a subcategory of AI, provides opportunities to accelerate and improve the process of discovering and developing new drugs. The use of AI in drug discovery is still in its early stages, but it has the potential to revolutionize the way new drugs are discovered and developed. As AI technology continues to evolve, it is likely that AI will play an even greater role in the future of drug discovery. AI is used to identify new drug targets, design new molecules, and predict the efficacy and safety of potential drugs. The inclusion of AI in drug discovery can screen millions of compounds in a matter of hours, identifying potential drug candidates that would have taken years to find using traditional methods. AI is highly utilized in the pharmaceutical industry by optimizing processes, reducing waste, and ensuring quality control. This review covers much-needed topics, including the different types of machine-learning techniques, their applications in drug discovery, and the challenges and limitations of using machine learning in this field. The state-of-the-art of AI-assisted pharmaceutical discovery is described, covering applications in structure and ligand-based virtual screening, de novo drug creation, prediction of physicochemical and pharmacokinetic properties, drug repurposing, and related topics. Finally, many obstacles and limits of present approaches are outlined, with an eye on potential future avenues for AI-assisted drug discovery and design.

Anti-Viral Activity of Bioactive Molecules of Silymarin against COVID-19 via In Silico Studies

The severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) infection drove the global coronavirus disease 2019 (COVID-19) pandemic, causing a huge loss of human life and a negative impact on economic development. It is an urgent necessity to explore potential drugs against viruses, such as SARS-CoV-2. Silymarin, a mixture of herb-derived polyphenolic flavonoids extracted from the milk thistle, possesses potent antioxidative, anti-apoptotic, and anti-inflammatory properties. Accumulating research studies have demonstrated the killing activity of silymarin against viruses, such as dengue virus, chikungunya virus, and hepatitis C virus. However, the anti-COVID-19 mechanisms of silymarin remain unclear. In this study, multiple disciplinary approaches and methodologies were applied to evaluate the potential mechanisms of silymarin as an anti-viral agent against SARS-CoV-2 infection. In silico approaches such as molecular docking, network pharmacology, and bioinformatic methods were incorporated to assess the ligand-protein binding properties and analyze the protein-protein interaction network. The DAVID database was used to analyze gene functions, such as the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) enrichment. TCMSP and GeneCards were used to identify drug target genes and COVID-19-related genes. Our results revealed that silymarin compounds, such as silybin A/B and silymonin, displayed triplicate functions against SARS-CoV-2 infection, including directly binding with human angiotensin-converting enzyme 2 (ACE2) to inhibit SARS-CoV-2 entry into the host cells, directly binding with viral proteins RdRp and helicase to inhibit viral replication and proliferation, and regulating host immune response to indirectly inhibit viral infection. Specifically, the targets of silymarin molecules in immune regulation were screened out, such as proinflammatory cytokines TNF and IL-6 and cell growth factors VEGFA and EGF. In addition, the molecular mechanism of drug-target protein interaction was investigated, including the binding pockets of drug molecules in human ACE2 and viral proteins, the formation of hydrogen bonds, hydrophobic interactions, and other drug-protein ligand interactions. Finally, the drug-likeness results of candidate molecules passed the criteria for drug screening. Overall, this study demonstrates the molecular mechanism of silymarin molecules against SARS-CoV-2 infection.

Fragment merging approach for the design of thiazole/thiazolidine clubbed pyrazoline derivatives as anti-inflammatory agents: Synthesis, biopharmacological evaluation and molecular modeling studies

Fragment merging approach was applied for the design of thiazole/thiazolidinone clubbed pyrazoline derivatives 5a-e, 6a-c, 7 and 10a-d as dual COX-2 and 5-LOX inhibitors. Compounds 5a, 6a, and 6b were the most potent and COX-2 selective inhibitors (IC50= 0.03-0.06 μM, SI = 282.7-472.9) with high activity against 5-LOX (IC50 = 4.36-4.86 μM), while compounds 5b and 10a were active and selective 5-LOX inhibitors with IC50 = 2.43 and 1.58 μM, respectively. In vivo assay and histopathological examination for most active candidate 6a revealed significant decrease in inflammation with higher safety profile in comparison to standard drugs. Compound 6a exhibited the same orientation and binding interactions as the reference COX-2 and 5-LOX inhibitors (celecoxib and quercetin, respectively). Consequently, compound 6a has been identified as a potential lead for further optimization and the development of safe and effective anti-inflammatory drugs.

Evaluation of Rapanone and Nectandrin B as novel inhibitors for targeting the metastatic regulator protein BACH1 using breast cancer cell line Mcf-7

Cancer formation is defined as the unrestrained proliferation of cells due to various factors acting as a causing agent. A limited number of over-expressed transcription factors are contributed to the development of numerous types of cancer. The metastatic regulator protein BTB And CNC Homology 1 (BACH1) is Cap 'N' Collar (CNC) and it belongs to a basic region leucine zipper (bZIP) family. The presence of the least level concentration of intracellular heme BACH1 forms heterodimers with musculo aponeurotic fibrosarcoma (sMAF) proteins and inhibits or induces the target gene expression. Based on the previous studies, BACH1 plays a critical player in the conditions of senescence and oxidative stress, cycling of cell life, heme degradation pathway and cancer, especially in metastasis. Discovering new anti-cancer drugs (identification of bioactive compounds) stages finally needs to inhibit the target protein. This present study is aimed to screen and identify stability, binding affinity and analysis of pharmacokinetics of selected compounds through structural screening, ADMET, DFT and MESP. From this study, it is revealed that Rapanone and Nectandrin B have the potential to alter the degree of gene expression via binding with the BACH1 allosteric region which will further change the degree of expression of BACH1 downstream target genes involved in the regulation of cancer progression particularly in metastasis. The two plant origin compounds Rapanone and Nectandrin B might be novel candidates for developing anti-cancer drugs. The predicted compounds were further validated through in-vitro experimental approaches.Communicated by Ramaswamy H. Sarma.

A Hybrid Structure-Based Machine Learning Approach for Predicting Kinase Inhibition by Small Molecules

Kinases have been the focus of drug discovery programs for three decades leading to over 70 therapeutic kinase inhibitors and biophysical affinity measurements for over 130,000 kinase-compound pairs. Nonetheless, the precise target spectrum for many kinases remains only partly understood. In this study, we describe a computational approach to unlocking qualitative and quantitative kinome-wide binding measurements for structure-based machine learning. Our study has three components: (i) a Kinase Inhibitor Complex (KinCo) data set comprising in silico predicted kinase structures paired with experimental binding constants, (ii) a machine learning loss function that integrates qualitative and quantitative data for model training, and (iii) a structure-based machine learning model trained on KinCo. We show that our approach outperforms methods trained on crystal structures alone in predicting binary and quantitative kinase-compound interaction affinities; relative to structure-free methods, our approach also captures known kinase biochemistry and more successfully generalizes to distant kinase sequences and compound scaffolds.

Uncovering anti-influenza mechanism of Ophiocordyceps sinensis using network pharmacology, molecular pharmacology, and metabolomics

Ophiocordyceps sinensis is a precious Chinese traditional herb with a long medicinal history. This study used UPLC-MS metabolomics to explore and compare the metabolic profiles of the stroma (OSBSz), sclerotium (OSBSh), and mycelium (OSBS) of O sinensis to analyze their differential metabolites and identified potential active components. Then combined with network pharmacology and molecular docking to explore the mechanism of differential metabolites with anti-influenza properties. The results indicate that the stroma, sclerotium, and mycelium showed significant differences in metabolites. The key pathways for differential metabolites were butanoate metabolism, thiamin metabolism, alanine, aspartate and glutamate metabolism, citrate cycle, and arginine biosynthesis. Protein-protein interaction analysis identified potential targets, including SRC, RHOA, HSP90AA1, VEGFA, ITGB1, PRKCA, and ITGA1, and the key protective pathways in-volved PI3K-Akt, HIF-1, influenza A, and Coronavirus disease 2019. The molecular docking results showed that the core metabolite D-(-)-glutamine has high binding affinity with SRC, RHOA, and EGFR, re-flecting the multi-component and multi-target network system of O sinensis. In short, the combination of metabonomics, network pharmacology and macromolecular docking technology provides a new way to explore the anti-influenza research of O sinensis. This is undoubtedly an important theoretical support for the clinical application of O sinensis in the future.

Protein-Ligand CH-π Interactions: Structural Informatics, Energy Function Development, and Docking Implementation

Here, we develop an empirical energy function based on quantum mechanical data for the interaction between methane and benzene that captures the contribution from CH-π interactions. Such interactions are frequently observed in protein-ligand crystal structures, particularly for carbohydrate ligands, but have been hard to quantify due to the absence of a model for CH-π interactions in typical molecular mechanical force fields or docking scoring functions. The CH-π term was added to the AutoDock Vina (AD VINA) scoring function enabling its performance to be evaluated against a cohort of more than 1600 occurrences in 496 experimental structures of protein-ligand complexes. By employing a conformational grid search algorithm, inclusion of the CH-π term was shown to improve the prediction of the preferred orientation of flexible ligands in protein-binding sites and to enhance the detection of carbohydrate-binding sites that display CH-π interactions. Last but not least, this term was also shown to improve docking performance for the CASF-2016 benchmark set and a carbohydrate set.

Recent Developments in Ultralarge and Structure-Based Virtual Screening Approaches

Drug development is a wide scientific field that faces many challenges these days. Among them are extremely high development costs, long development times, and a small number of new drugs that are approved each year. New and innovative technologies are needed to solve these problems that make the drug discovery process of small molecules more time and cost efficient, and that allow previously undruggable receptor classes to be targeted, such as protein-protein interactions. Structure-based virtual screenings (SBVSs) have become a leading contender in this context. In this review, we give an introduction to the foundations of SBVSs and survey their progress in the past few years with a focus on ultralarge virtual screenings (ULVSs). We outline key principles of SBVSs, recent success stories, new screening techniques, available deep learning-based docking methods, and promising future research directions. ULVSs have an enormous potential for the development of new small-molecule drugs and are already starting to transform early-stage drug discovery.

Simulation-Based Research on Phytoconstituents of Embelia ribes Targeting Proteins with Pathophysiological Implications in Rheumatoid Arthritis

Rheumatoid arthritis (RA) is a heterogeneous inflammatory disease with an autoimmune origin and an incompletely elucidated pathophysiological mechanism. RA pharmacotherapy is based on chemically or biologically active substances that provide clinical alleviation and remission, but the disease is still incurable. As a result, there remains a need for significant therapeutic development, and adjuvant therapies may play an essential role in the search for novel RA treatment strategies. The aim of the present study was to investigate potential phytocompounds and phytocompound derivates as RA treatment agents, using in silico methodologies. In this regard, five phytoconstituents identified in different structures of Embelia ribes were evaluated by in silico methods for their potential action on target proteins of therapeutic interest in RA. The methodology involved identifying the phytocompound with the highest binding toward the target protein via molecular docking using AutoDock Vina 1.5.7, followed by a ligand-based virtual screening based on the structure of the most promising phytocompound using SwissSimilarity. This process led to the identification of ligands that are not currently utilized in medical practice, but that might have the potential to be used in the management of RA after further extensive experimental endorsements. ZINC000004024651 showed the highest binding affinity for the Bruton's tyrosine kinase protein, followed by ZINC000000434197 for p38 mitogen-activated protein kinases, ZINC000087606977 for interleukin-1 receptor-associated kinase 4, and ZINC000014728393 for matrix metallopeptidase 9, the latter two showing higher affinity than the co-crystallized compound. The relatively high affinities to target proteins and the pharmacokinetic data obtained by in silico studies using SwisADME suggest a first step for the inclusion of promising new compounds in various more advanced studies, leading to the evaluation of efficacy and safety profiles.

SS-GNN: A Simple-Structured Graph Neural Network for Affinity Prediction

Efficient and effective drug-target binding affinity (DTBA) prediction is a challenging task due to the limited computational resources in practical applications and is a crucial basis for drug screening. Inspired by the good representation ability of graph neural networks (GNNs), we propose a simple-structured GNN model named SS-GNN to accurately predict DTBA. By constructing a single undirected graph based on a distance threshold to represent protein-ligand interactions, the scale of the graph data is greatly reduced. Moreover, ignoring covalent bonds in the protein further reduces the computational cost of the model. The graph neural network-multilayer perceptron (GNN-MLP) module takes the latent feature extraction of atoms and edges in the graph as two mutually independent processes. We also develop an edge-based atom-pair feature aggregation method to represent complex interactions and a graph pooling-based method to predict the binding affinity of the complex. We achieve state-of-the-art prediction performance using a simple model (with only 0.6 M parameters) without introducing complicated geometric feature descriptions. SS-GNN achieves Pearson's R_p = 0.853 on the PDBbind v2016 core set, outperforming state-of-the-art GNN-based methods by 5.2%. Moreover, the simplified model structure and concise data processing procedure improve the prediction efficiency of the model. For a typical protein-ligand complex, affinity prediction takes only 0.2 ms. All codes are freely accessible at https://github.com/xianyuco/SS-GNN.

Molecular docking-based interaction studies on imidazo[1,2-a] pyridine ethers and squaramides as anti-tubercular agents

Development of new anti-tubercular agents is required in the wake of resistance to the existing and newly approved drugs through novel-validated targets like ATP synthase, etc. The major limitation of poor correlation between docking scores and biological activity by SBDD was overcome by a novel approach of quantitatively correlating the interactions of different amino acid residues present in the target protein structure with the activity. This approach well predicted the ATP synthase inhibitory activity of imidazo[1,2-a] pyridine ethers and squaramides (r = 0.84) in terms of Glu65b interactions. Hence, the models were developed on combined (r = 0.78), and training (r = 0.82) sets of 52, and 27 molecules, respectively. The training set model well predicted the diverse dataset (r = 0.84), test set (r = 0.755), and, external dataset (r_ext = 0.76). This model predicted three compounds from a focused library generated by incorporating the essential features of the ATP synthase inhibition with the pIC₅₀ values in the range of 0.0508-0.1494 µM. Molecular dynamics simulation studies ascertain the stability of the protein structure and the docked poses of the ligands. The developed model(s) may be useful in the identification and optimization of novel compounds against TB.

Structure-Based Design of Novel MAO-B Inhibitors: A Review

With the significant growth of patients suffering from neurodegenerative diseases (NDs), novel classes of compounds targeting monoamine oxidase type B (MAO-B) are promptly emerging as distinguished structures for the treatment of the latter. As a promising function of computer-aided drug design (CADD), structure-based virtual screening (SBVS) is being heavily applied in processes of drug discovery and development. The utilization of molecular docking, as a helping tool for SBVS, is providing essential data about the poses and the occurring interactions between ligands and target molecules. The current work presents a brief discussion of the role of MAOs in the treatment of NDs, insight into the advantages and drawbacks of docking simulations and docking software, and a look into the active sites of MAO-A and MAO-B and their main characteristics. Thereafter, we report new chemical classes of MAO-B inhibitors and the essential fragments required for stable interactions focusing mainly on papers published in the last five years. The reviewed cases are separated into several chemically distinct groups. Moreover, a modest table for rapid revision of the revised works including the structures of the reported inhibitors together with the utilized docking software and the PDB codes of the crystal targets applied in each study is provided. Our work could be beneficial for further investigations in the search for novel, effective, and selective MAO-B inhibitors.

Recent Advances in Computer-Aided Structure-Based Drug Design on Ion Channels

Ion channels play important roles in fundamental biological processes, such as electric signaling in cells, muscle contraction, hormone secretion, and regulation of the immune response. Targeting ion channels with drugs represents a treatment option for neurological and cardiovascular diseases, muscular degradation disorders, and pathologies related to disturbed pain sensation. While there are more than 300 different ion channels in the human organism, drugs have been developed only for some of them and currently available drugs lack selectivity. Computational approaches are an indispensable tool for drug discovery and can speed up, especially, the early development stages of lead identification and optimization. The number of molecular structures of ion channels has considerably increased over the last ten years, providing new opportunities for structure-based drug development. This review summarizes important knowledge about ion channel classification, structure, mechanisms, and pathology with the main focus on recent developments in the field of computer-aided, structure-based drug design on ion channels. We highlight studies that link structural data with modeling and chemoinformatic approaches for the identification and characterization of new molecules targeting ion channels. These approaches hold great potential to advance research on ion channel drugs in the future.

Past, Present, and Future Perspectives on Computer-Aided Drug Design Methodologies

The application of computational approaches in drug discovery has been consolidated in the last decades. These families of techniques are usually grouped under the common name of "computer-aided drug design" (CADD), and they now constitute one of the pillars in the pharmaceutical discovery pipelines in many academic and industrial environments. Their implementation has been demonstrated to tremendously improve the speed of the early discovery steps, allowing for the proficient and rational choice of proper compounds for a desired therapeutic need among the extreme vastness of the drug-like chemical space. Moreover, the application of CADD approaches allows the rationalization of biochemical and interactive processes of pharmaceutical interest at the molecular level. Because of this, computational tools are now extensively used also in the field of rational 3D design and optimization of chemical entities starting from the structural information of the targets, which can be experimentally resolved or can also be obtained with other computer-based techniques. In this work, we revised the state-of-the-art computer-aided drug design methods, focusing on their application in different scenarios of pharmaceutical and biological interest, not only highlighting their great potential and their benefits, but also discussing their actual limitations and eventual weaknesses. This work can be considered a brief overview of computational methods for drug discovery.

Molecular Docking and Dynamics Simulation Studies Predict Potential Anti-ADAR2 Inhibitors: Implications for the Treatment of Cancer, Neurological, Immunological and Infectious Diseases

Altered RNA editing has been linked to several neurodevelopmental disorders, including autism spectrum disorder (ASD) and intellectual disability, in addition to depression, schizophrenia, some cancers, viral infections and autoimmune disorders. The human ADAR2 is a potential therapeutic target for managing these various disorders due to its crucial role in adenosine to inosine editing. This study applied consensus scoring to rank potential ADAR2 inhibitors after performing molecular docking with AutoDock Vina and Glide (Maestro), using a library of 35,161 compounds obtained from traditional Chinese medicine. A total of 47 compounds were predicted to be good binders of the human ADAR2 and had insignificant toxicity concerns. Molecular dynamics (MD) simulations, including the molecular mechanics Poisson–Boltzmann surface area (MM/PBSA) procedure, also emphasized the binding of the shortlisted compounds. The potential compounds had plausible binding free energies ranging from −81.304 to −1068.26 kJ/mol from the MM/PBSA calculations. ZINC000085511995, a naphthoquinone had more negative binding free energy (−1068.26 kJ/mol) than inositol hexakisphosphate (IHP) [−873.873 kJ/mol], an agonist and a strong binder of ADAR2. The potential displacement of IHP by ZINC000085511995 in the IHP binding site of ADAR2 could be explored for possible deactivation of ADAR2. Bayesian-based biological activity prediction corroborates the neuropharmacological, antineoplastic and antiviral activity of the potential lead compounds. All the potential lead compounds, except ZINC000014612330 and ZINC000013462928, were predicted to be inhibitors of various deaminases. The potential lead compounds also had probability of activity (Pa) > 0.442 and probability of inactivity (Pi) < 0.116 values for treating acute neurologic disorders, except for ZINC000085996580 and ZINC000013462928. Pursuing these compounds for their anti-ADAR2 activities holds a promising future, especially against neurological disorders, some cancers and viral infections caused by RNA viruses. Molecular interaction, hydrogen bond and per-residue decomposition analyses predicted Arg400, Arg401, Lys519, Trp687, Glu689, and Lys690 as hot-spot residues in the ADAR2 IHP binding site. Most of the top compounds were observed to have naphthoquinone, indole, furanocoumarin or benzofuran moieties. Serotonin and tryptophan, which are beneficial in digestive regulation, improving sleep cycle and mood, are indole derivatives. These chemical series may have the potential to treat neurological disorders, prion diseases, some cancers, specific viral infections, metabolic disorders and eating disorders through the disruption of ADAR2 pathways. A total of nine potential lead compounds were shortlisted as plausible modulators of ADAR2.