Analysis and comparison of 2D fingerprints: insights into database screening performance using eight fingerprint methods

SmartCADD: AI-QM Empowered Drug Discovery Platform with Explainability

Artificial intelligence (AI) has emerged as a pivotal force in enhancing productivity across various sectors, with its impact being profoundly felt within the pharmaceutical and biotechnology domains. Despite AI's rapid adoption, its integration into scientific research faces resistance due to myriad challenges: the opaqueness of AI models, the intricate nature of their implementation, and the issue of data scarcity. In response to these impediments, we introduce SmartCADD, an innovative, open-source virtual screening platform that combines deep learning, computer-aided drug design (CADD), and quantum mechanics methodologies within a user-friendly Python framework. SmartCADD is engineered to streamline the construction of comprehensive virtual screening workflows that incorporate a variety of formerly independent techniques─spanning ADMET property predictions, de novo 2D and 3D pharmacophore modeling, molecular docking, to the integration of explainable AI mechanisms. This manuscript highlights the foundational principles, key functionalities, and the unique integrative approach of SmartCADD. Furthermore, we demonstrate its efficacy through a case study focused on the identification of promising lead compounds for HIV inhibition. By democratizing access to advanced AI and quantum mechanics tools, SmartCADD stands as a catalyst for progress in pharmaceutical research and development, heralding a new era of innovation and efficiency.

Estrogen receptor alpha (ER-α) antagonistic activity of phytoconstituents from Potentilla atrosanguinea and Potentilla fulgens in breast cancer

The Potentilla genus has long been used traditionally as food and a folklore medicine. In the present study, aerial parts of two Potentilla species, Potentilla fulgens and Potentilla atrosanguinea, of western Himalayan origin, were studied for their anti-breast cancer activity. Ethyl acetate (PAA-EA, PFA-EA), methanolic (PAA-ME, PFA-ME) and hydro-methanolic extract (PAA-HM, PFA-HM) of the plants were tested for their antiproliferative activities against MCF-7 and T-47D breast cancer cell lines. The extracts showed good antiproliferative activity against ER-α dominant breast cancer cell line T-47D, having IC50 values 6.19 ± 0.01 to 33.23 ± 0.04 μg/ml. Eight compounds were isolated, characterized, and quantified from ethyl acetate and methanolic extracts by column chromatography, 1D, 2D-NMR, HRMS and TLC densitometric analysis. Two compounds (4 and 6) have shown better antiproliferative activity than standard bazedoxifene and were further evaluated for their ER-α binding affinity via-fluorescence polarization-based competitive binding assay. The antiestrogenic properties of both compounds were assessed using western blotting. Compounds 4 and 6 were found to have significant affinity for the ER-α and managed to decrease its expression by 38 and 54% respectively. Compounds 4 and 6 also had good stability and reactivity as measured by minimal fluctuations in molecular dynamic simulation analysis, a good dock score in molecular docking, and a respectable HOMO-LUMO energy gap in DFT calculations. Compounds 4 and 6 have shown reliable results and can be used in the development of natural product-based anti-breast cancer agents.

Pharmacophore mapping, 3D QSAR, molecular docking, and ADME prediction studies of novel Benzothiazinone derivatives

In the quest to combat tuberculosis, DprE1, a challenging target for novel anti-tubercular agents due to its small size and membrane location, has been a focus of research. DprE1 catalyzes the transformation of DPR into Ketoribose DPX, with Benzothiazinone emerging as a potent pharmacophore for inhibiting DprE1. Clinical trial drugs such as BTZ043, BTZ038, PBTZ169, and TMC-207 have shown promising results as DprE1 inhibitors. This study employed pharmacophore mapping of Pyrazolopyridine, Dinitrobenzamide, and Benzothiazinone derivatives to identify crucial features for eliciting a biological response. Benzothiazinone (Ligand code: 73) emerged as a reference ligand with a fitness score of 3.000. ROC analysis validated the pharmacophore with an excellent score of 0.71. To build a 3D QSAR model, a series of Benzothiazinone congeneric derivatives were explored. The model exhibited strong performance, with a standard deviation of 0.1531, a correlation coefficient for the training set (R2) value of 0.9754, and a correlation coefficient for test set Q2 value of 0.7632, indicating robust predictive capabilities. Contour maps guided the design of novel benzothiazinone derivatives, emphasizing steric, electrostatic, hydrophobic, H-bond acceptor, and H-bond donor groups for structure-activity relationships. Docking studies against PDB ID: 4NCR demonstrated favorable scores, with interactions aligning well with the in-built ligand 26 J. Docking validation via RMSD values supported the reliability of the docking results. This comprehensive approach aids in the design of novel benzothiazinone derivatives with potential anti-tubercular properties, contributing to the development of novel anti-tubercular agents which can be pivotal in the eradication of tuberculosis.

AC-ModNet: Molecular Reverse Design Network Based on Attribute Classification

Deep generative models are becoming a tool of choice for exploring the molecular space. One important application area of deep generative models is the reverse design of drug compounds for given attributes (solubility, ease of synthesis, etc.). Although there are many generative models, these models cannot generate specific intervals of attributes. This paper proposes a AC-ModNet model that effectively combines VAE with AC-GAN to generate molecular structures in specific attribute intervals. The AC-ModNet is trained and evaluated using the open 250K ZINC dataset. In comparison with related models, our method performs best in the FCD and Frag model evaluation indicators. Moreover, we prove the AC-ModNet created molecules have potential application value in drug design by comparing and analyzing them with medical records in the PubChem database. The results of this paper will provide a new method for machine learning drug reverse design.

In Silico Assisted Identification, Synthesis, and In Vitro Pharmacological Characterization of Potent and Selective Blockers of the Epilepsy-Associated KCNT1 Channel

Gain-of-function (GoF) variants in KCNT1 channels cause severe, drug-resistant forms of epilepsy. Quinidine is a known KCNT1 blocker, but its clinical use is limited due to severe drawbacks. To identify novel KCNT1 blockers, a homology model of human KCNT1 was built and used to screen an in-house library of compounds. Among the 20 molecules selected, five (CPK4, 13, 16, 18, and 20) showed strong KCNT1-blocking ability in an in vitro fluorescence-based assay. Patch-clamp experiments confirmed a higher KCNT1-blocking potency of these compounds when compared to quinidine, and their selectivity for KCNT1 over hERG and Kv7.2 channels. Among identified molecules, CPK20 displayed the highest metabolic stability; this compound also blocked KCNT2 currents, although with a lower potency, and counteracted GoF effects prompted by 2 recurrent epilepsy-causing KCNT1 variants (G288S and A934T). The present results provide solid rational basis for future design of novel compounds to counteract KCNT1-related neurological disorders.

Predicting ADMET Properties from Molecule SMILE: A Bottom-Up Approach Using Attention-Based Graph Neural Networks

Understanding the pharmacokinetics, safety and efficacy of candidate drugs is crucial for their success. One key aspect is the characterization of absorption, distribution, metabolism, excretion and toxicity (ADMET) properties, which require early assessment in the drug discovery and development process. This study aims to present an innovative approach for predicting ADMET properties using attention-based graph neural networks (GNNs). The model utilizes a graph-based representation of molecules directly derived from Simplified Molecular Input Line Entry System (SMILE) notation. Information is processed sequentially, from substructures to the whole molecule, employing a bottom-up approach. The developed GNN is tested and compared with existing approaches using six benchmark datasets and by encompassing regression (lipophilicity and aqueous solubility) and classification (CYP2C9, CYP2C19, CYP2D6 and CYP3A4 inhibition) tasks. Results show the effectiveness of our model, which bypasses the computationally expensive retrieval and selection of molecular descriptors. This approach provides a valuable tool for high-throughput screening, facilitating early assessment of ADMET properties and enhancing the likelihood of drug success in the development pipeline.

From NMR to AI: Designing a Novel Chemical Representation to Enhance Machine Learning Predictions of Physicochemical Properties

A novel approach to the utilization of nuclear magnetic resonance (NMR) spectroscopy data in the prediction of logD through machine learning algorithms is shown. In the analysis, a data set of 754 chemical compounds, organized into 30 clusters, was evaluated using advanced machine learning models, such as Support Vector Regression (SVR), Gradient Boosting, and AdaBoost, and comprehensive validation and testing methods were employed, including 10-fold cross-validation, bootstrapping, and leave-one-out. The study revealed the superior performance of the Bucket Integration method for dimensionality reduction, consistently yielding the lowest root mean square error (RMSE) across all data sets and normalization schemes. The SVR prediction models demonstrated remarkable computational efficiency and low cost, with the best RMSE value reaching 0.66. Our best model outperformed existing tools like JChem Suite's logD Predictor (0.91) and CplogD (1.27), and a comparison with traditional molecular representations yielded a comparable RMSE (0.50), emphasizing the robustness of our NMR data integration. The widespread availability of NMR data in pharmaceutical and industrial research presents an untapped resource for predictive modeling, highlighting the need for accessible methodologies like ours that complement the analytical toolbox beyond conventional 2D approaches. Our approach, designed to leverage the rich spatial data from NMR spectroscopy, provides additional insights and enriches drug discovery and computational chemistry with a freely accessible tool.

Discovery of novel covalent inhibitors of DJ-1 through hybrid virtual screening

Background: DJ-1 is a ubiquitously expressed protein with multiple functions. Its overexpression has been associated with the occurrence of several cancers, positioning DJ-1 as a promising therapeutic target for cancer treatment. Methods: To find novel inhibitors of DJ-1, we employed a hybrid virtual screening strategy that combines structure-based and ligand-based virtual screening on a comprehensive compound library. Results: In silico study identified six hit compounds as potential DJ-1 inhibitors that were assessed in vitro at the cellular level. Compound 797780-71-3 exhibited antiproliferation activity in ACHN cells with an IC50 value of 12.18 μM and was able to inhibit the Wnt signaling pathway. This study discovers a novel covalent inhibitor for DJ-1 and paves the way for further optimization.

Integration of fingerprint-based similarity searching and kernel-based partial least squares analysis to predict inhibitory activity against CSK, HER2, JAK1, JAK2, and JAK3

Fingerprint-based similarity searching is an important strategy for virtual screening in drug discovery. In the present study, we carried out a systematic virtual screening study, followed by the establishment of kernel-based partial least square (KPLS) analysis prediction models for five tyrosine kinase drug targets, C-terminal SRC kinase (CSK), human epidermal growth factor 2 (HER2), and Janus kinases 1, 2, and 3 (JAK1, JAK2, and JAK3), using a dataset of 3688 compounds. These kinases are important drug discovery targets, particularly as HER2 has been validated for the treatment of metastatic breast cancer, JAK inhibitors have been validated for the clinical management of arthritis and autoimmune diseases, and CSK has been found to play an important role in bone remodeling in arthritis. We conducted similarity screenings with the most active molecule for each target in the dataset as a query using eight (8) types of two-dimensional (2D) molecular fingerprints, comprising seven Hashed fingerprints, Linear, Dendritic, Radial, Pairwise, Triplet, Torsion, and MOLSPRINT2D, and one Structural keys fingerprint, MACCS. The top ranked 1% of compounds from each target's similarity screening results was used to set up kernel-based partial least square (KPLS) prediction models, with q2 values up to 0.8. The best KPLS model for each target was selected based on its predictive ability and boot strapping results and used for prediction. This integrated study approach combining similarity screening with KPLS analysis has a high potential to enhance the accuracy and efficiency of virtual screening and thus improve the drug discovery process.

2-Phenylcyclopropylmethylamine (PCPMA) as a privileged scaffold for central nervous system drug design

The use of privileged scaffolds in medicinal chemistry is an effective way to accelerate the drug discovery process, especially at the hit/lead optimization stage. 2-Phenylcyclopropylmethylamine (PCPMA) is a less commonly used chemical scaffold in medicinal chemistry, but many PCPMA-containing compounds exert therapeutic effects for various diseases, in particular central nervous system (CNS) diseases such as depression, schizophrenia, sleep disorder, and Parkinson's disease. The backbone of the PCPMA scaffold enables a unique structure of an amino group linked to a benzene ring through an alkyl linker, making it a useful template for the design of bioactive compounds especially for CNS drug targets including aminergic GPCRs and transporters. This review summarizes the medicinal chemistry studies of PCPMA-containing drugs and drug-like molecules, their mechanisms of action, and biological activities. We conclude that PCPMA is a unique and useful privileged scaffold for CNS drug design.

A simple UHPLC-MS/MS method for determination of SET2, a selective antagonist of TRPV2 receptor, in rat plasma samples

Targeting the transient receptor potential vanilloid 2 channels (TRPV2) in order to alleviate or reverse the course of several diseases including multiple cancers, cardiovascular, immunological, or neurological disorders have been a matter of focus for several years now. SET2, a selective TRPV2 inhibitor, represents an innovative molecule which came into recognition in 2019 and seems to be a promising therapeutic modality in cancer and cardiac diseases. Drug discovery and bioanalysis in clinical environment demands simple, excellent, highly reliable, fast, sensitive, and selective analytical approaches which enable unambiguous identification and quantification of demanded molecule. Here, a targeted ultra-high-performance liquid chromatography - tandem mass spectrometry with electrospray ionization was developed for the quantification of SET2 in plasma samples. The developed method enabled analysis of approx. 15 samples within one hour. Simplicity of the whole analytical procedure can be emphasized by a very simple sample pretreatment based only on the protein precipitation with organic acid (here, 2 M tricholoroacetic acid). The validation procedure was characterized by promising validation parameters and excellent sensitivity what was documented by the limit of detection value at pg.mL-1 concentration level. Analytical validation reported intra- and interday accuracy < 15 % for all quality control samples concentration levels. Similarly, excellent level of intra- (0.1 - 4.8 %) and interday (0.5 - 3.3 %) precision for the tested quality control samples was obtained. The applicability of the developed method was proven by quantifying SET2 concentration levels in plasma samples obtained from Wistar rats that were administered this drug intraperitoneally at a dose of 25 mg/kg. We expect that our new analytical method represents a very attractive tool that could be easily implemented in pharmacokinetics studies and/or therapeutic drug monitoring. Moreover, its applicability was confirmed by the new practicability evaluation metric tool.

Engineering novel scaffolds for specific HDAC11 inhibitors against metabolic diseases exploiting deep learning, virtual screening, and molecular dynamics simulations

The prevalence of metabolic diseases is increasing at a frightening rate year by year. The burgeoning development of deep learning enables drug design to be more efficient, selective, and structurally novel. The critical relevance of Histone deacetylase 11 (HDAC11) to the pathogenesis of several metabolic diseases makes it a promising drug target for curbing metabolic disorders. The present study aims to design new specific HDAC11 inhibitors for the treatment of metabolic diseases. Deep learning was performed to learn the properties of existing HDAC11 inhibitors and yield a novel compound library containing 23,122 molecules. Subsequently, the compound library was screened by ADMET properties, Lipinski & Veber rules, traditional machine classification models, and molecular docking, and 10 compounds were screened as candidate HDAC11 inhibitors. The stability of the 10 new molecules was further evaluated by deploying RMSD, RMSF, MM/GBSA, free energy landscape mapping, and PCA analysis in molecular dynamics simulations. As a result, ten compounds, Cpd_17556, Cpd_2184, Cpd_8907, Cpd_7771, Cpd_14959, Cpd_7108, Cpd_12383, Cpd_13153, Cpd_14500and Cpd_21811, were characterized as good HDAC11 inhibitors and are expected to be promising drug candidates for metabolic disorders, and further in vitro, in vivo and clinical trials to demonstrate in the future.

Identification of a druggable site on GRP78 at the GRP78-SARS-CoV-2 interface and virtual screening of compounds to disrupt that interface

SARS-CoV-2, the virus that causes COVID-19, led to a global health emergency that claimed the lives of millions. Despite the widespread availability of vaccines, the virus continues to exist in the population in an endemic state which allows for the continued emergence of new variants. Most of the current vaccines target the spike glycoprotein interface of SARS-CoV-2, creating a selection pressure favoring viral immune evasion. Antivirals targeting other molecular interactions of SARS-CoV-2 can help slow viral evolution by providing orthogonal selection pressures on the virus. GRP78 is a host auxiliary factor that mediates binding of the SARS-CoV-2 spike protein to human cellular ACE2, the primary pathway of cell infection. As GRP78 forms a ternary complex with SARS-CoV-2 spike protein and ACE2, disrupting the formation of this complex is expected to hinder viral entry into host cells. Here, we developed a model of the GRP78-Spike RBD-ACE2 complex. We then used that model together with hot spot mapping of the GRP78 structure to identify the putative binding site for spike protein on GRP78. Next, we performed structure-based virtual screening of known drug/candidate drug libraries to identify binders to GRP78 that are expected to disrupt spike protein binding to the GRP78, and thereby preventing viral entry to the host cell. A subset of these compounds has previously been shown to have some activity against SARS-CoV-2. The identified hits are starting points for the further development of novel SARS-CoV-2 therapeutics, potentially serving as proof-of-concept for GRP78 as a potential drug target for other viruses.

Comparative Structure-Based Virtual Screening Utilizing Optimized AlphaFold Model Identifies Selective HDAC11 Inhibitor

HDAC11 is a class IV histone deacylase with no crystal structure reported so far. The catalytic domain of HDAC11 shares low sequence identity with other HDAC isoforms, which makes conventional homology modeling less reliable. AlphaFold is a machine learning approach that can predict the 3D structure of proteins with high accuracy even in absence of similar structures. However, the fact that AlphaFold models are predicted in the absence of small molecules and ions/cofactors complicates their utilization for drug design. Previously, we optimized an HDAC11 AlphaFold model by adding the catalytic zinc ion and minimization in the presence of reported HDAC11 inhibitors. In the current study, we implement a comparative structure-based virtual screening approach utilizing the previously optimized HDAC11 AlphaFold model to identify novel and selective HDAC11 inhibitors. The stepwise virtual screening approach was successful in identifying a hit that was subsequently tested using an in vitro enzymatic assay. The hit compound showed an IC50 value of 3.5 µM for HDAC11 and could selectively inhibit HDAC11 over other HDAC subtypes at 10 µM concentration. In addition, we carried out molecular dynamics simulations to further confirm the binding hypothesis obtained by the docking study. These results reinforce the previously presented AlphaFold optimization approach and confirm the applicability of AlphaFold models in the search for novel inhibitors for drug discovery.

Discovery of Novel Inhibitors of BRD4 for Treating Prostate Cancer: A Comprehensive Case Study for Considering Water Networks in Virtual Screening and Drug Design

Androgen receptor (AR) is the primary target for treating prostate cancer (PCa), which inevitably progresses due to drug-resistant mutations. Bromodomain-containing protein 4 (BRD4) has been a new potential drug target for PCa treatment. Herein, we report the rational design and discovery of novel BRD4 inhibitors through computer-aided drug design (CADD), and a hit compound SQ-1 (IC50 = 676 nM) was identified by structure-based virtual screening (SBVS) with the conserved water network. To optimize the structure of SQ-1, the free energy landscape was constructed, and the binding mechanism was explored by characterizing the water profile and the dissociation mechanism. Finally, the compound SQ-17 with improved inhibitory activity (IC50 < 100 nM) was discovered, which showed potent antiproliferative activity against LNCaP. These data highlighted a successful attempt to identify and optimize a small molecule by comprehensive CADD application and provided essential clues for developing novel therapeutics for PCa treatment.

Enabling Systemic Identification and Functionality Profiling for Cdc42 Homeostatic Modulators

Homeostatic modulation is pivotal in modern therapeutics. However, the discovery of bioactive materials to achieve this functionality is often random and unpredictive. Here, we enabled a systemic identification and functional classification of chemicals that elicit homeostatic modulation of signaling through Cdc42, a classical small GTPase of Ras superfamily. Rationally designed for high throughput screening, the capture of homeostatic modulators (HMs) along with molecular re-docking uncovered at least five functionally distinct classes of small molecules. This enabling led to partial agonists, hormetic agonists, bona fide activators and inhibitors, and ligand-enhanced agonists. Novel HMs exerted striking functionality in bradykinin-Cdc42 activation of actin remodelingand modified Alzheimer's disease-like behavior in mouse model. This concurrent computer-aided and experimentally empowered HM profiling highlights a model path for predicting HM landscape.

One Sentence Summary

With concurrent experimental biochemical profiling and in silico computer-aided drug discovery (CADD) analysis, this study enabled a systemic identification and holistic classification of Cdc42 homeostatic modulators (HMs) and demonstrated the power of CADD to predict HM classes that can mimic the pharmacological functionality of interests.

Introduction

Maintainingbody homeostasisis the ultimate keyto health. Thereare rich resources of bioactive materials for this functionality from both natural and synthetic chemical repertories including partial agonists (PAs) and various allosteric modulators. These homeostatic modulators (HMs) play a unique role in modern therapeutics for human diseases such as mental disorders and drug addiction. Buspirone, for example, acts as a PA for serotonin 5-HT 1A receptor but is an antagonist of the dopamine D 2 receptor. Such medical useto treat general anxietydisorders (GADs) has become one of the most-commonly prescribed medications. However, most HMs in current uses target membrane proteins and are often derived from random discoveries. HMs as therapeutics targeting cytoplasmic proteins are even more rare despite that they are in paramount needs (e. g. targeting Ras superfamily small GTPases).

Rationale

Cdc42, a classical member of small GTPases of Ras superfamily, regulates PI3K-AKT and Raf-MEK-ERK pathways and has been implicated in various neuropsychiatric and mental disorders as well as addictive diseases and cancer. We previously reported the high-throughput in-silico screening followed by biological characterization of novel small molecule modulators (SMMs) of Cdc42-intersectin (ITSN) protein-protein interactions (PPIs). Based on a serendipitously discovered SMM ZCL278 with PA profile as a model compound, we hypothesized that there are more varieties of such HMs of Cdc42 signaling, and the model HMs can be defined by their distinct Cdc42-ITSN binding mechanisms using computer-aided drug discovery (CADD) analysis. We further reasoned that molecular modeling coupled with experimental profiling can predict HM spectrum and thus open the door for the holistic identification and classification of multifunctional cytoplasmic target-dependent HMs as therapeutics.

Results

The originally discovered Cdc42 inhibitor ZCL278 displaying PA properties prompted the inquiry whether this finding represented a random encounter of PAs or whether biologically significant PAs can be widely present. The top ranked compounds were initially defined by structural fitness and binding scores to Cdc42. Because higher binding scores do not necessarily translate to higher functionality, we performed exhaustive experimentations with over 2,500 independent Cdc42-GEF (guanine nucleotide exchange factor) assays to profile the GTP loading activities on all 44 top ranked compounds derived from the SMM library. The N-MAR-GTP fluorophore-based Cdc42-GEF assay platform provided the first glimpse of the breadth of HMs. A spectrum of Cdc42 HMs was uncovered that can be categorized into five functionally distinct classes: Class I-partial competitive agonists, Class II-hormetic agonists, Class III- bona fide inhibitors (or inverse agonists), Class IV- bona fide activators or agonists, and Class V-ligand-enhanced agonists. Remarkably, model HMs such as ZCL278, ZCL279, and ZCL367 elicited striking biological functionality in bradykinin-Cdc42 activation of actin remodeling and modified Alzheimer's disease (AD)-like behavior in mouse model. Concurrently, we applied Schrödinger-enabled analyses to perform CADD predicted classification of Cdc42 HMs. We modified the classic molecular docking to instill a preferential binding pocket order (PBPO) of Cdc42-ITSN, which was based on the five binding pockets in interface of Cdc42-ITSN. We additionally applied a structure-based pharmacophore hypothesis generation for the model compounds. Then, using Schrödinger's Phase Shape, 3D ligand alignments assigned HMs to Class I, II, III, IV, and V compounds. In this HM library compounds, PBPO, matching pharmacophoric featuring, and shape alignment, all put ZCL993 in Class II compound category, which was confirmed in the Cdc42-GEF assay.

Conclusion

HMs can target diseased cells or tissues while minimizing impacts on tissues that are unaffected. Using Cdc42 HM model compounds as a steppingstone, GTPase activation-based screening of SMM library uncovered five functionally distinct Cdc42 HM classes among which novel efficacies towards alleviating dysregulated AD-like features in mice were identified. Furthermore, molecular re-docking of HM model compounds led to the concept of PBPO. The CADD analysis with PBPO revealed similar profile in a color-coded spectrum to these five distinct classes of Cdc42 HMs identified by biochemical functionality-based screening. The current study enabled a systemic identification and holistic classification of Cdc42 HMs and demonstrated the power of CADD to predict an HM category that can mimic the pharmacological functionality of interests. With artificial intelligence/machine learning (AI/ML) on the horizon to mirror experimental pharmacological discovery like AlphaFold for protein structure prediction, our study highlights a model path to actively capture and profile HMs in potentially any PPI landscape.

Graphic Abstract

Identification and functional classification of Cdc42 homeostatic modulators HMs

Using Cdc42 HM model compounds as reference, GTPase activation-based screening of compound libraries uncovered five functionally distinct Cdc42 HM classes. HMs showed novel efficacies towards alleviating dysregulated Alzheimer's disease (AD)-like behavioral and molecular deficits. In parallel, molecular re-docking of HM model compounds established their preferential binding pocket orders (PBPO). PBPO-based profiling (Red reflects the most, whereas green reflects the least, preferable binding pocket) revealed trends of similar pattern to the five classes from the functionality-based classification.

The Pivotal Distinction between Antagonists' and Agonists' Binding into Dopamine D4 Receptor-MD and FMO/PIEDA Studies

The dopamine D4 receptor (D4R) is a promising therapeutic target in widespread diseases, and the search for novel agonists and antagonists appears to be clinically relevant. The mechanism of binding to the receptor (R) for antagonists and agonists varies. In the present study, we conducted an in-depth computational study, teasing out key similarities and differences in binding modes, complex dynamics, and binding energies for D4R agonists and antagonists. The dynamic network method was applied to investigate the communication paths between the ligand (L) and G-protein binding site (GBS) of human D4R. Finally, the fragment molecular orbitals with pair interaction energy decomposition analysis (FMO/PIEDA) scheme was used to estimate the binding energies of L-R complexes. We found that a strong salt bridge with D3.32 initiates the inhibition of the dopamine D4 receptor. This interaction also occurs in the binding of agonists, but the change in the receptor conformation to the active state starts with interaction with cysteine C3.36. Such a mechanism may arise in the case of agonists unable to form a hydrogen bond with the serine S5.46, considered, so far, to be crucial in the activation of GPCRs. The energy calculations using the FMO/PIEDA method indicate that antagonists show higher residue occupancy of the receptor binding site than agonists, suggesting they could form relatively more stable complexes. Additionally, antagonists were characterized by repulsive interactions with S5.46 distinguishing them from agonists.

Fingerprinting Interactions between Proteins and Ligands for Facilitating Machine Learning in Drug Discovery

Molecular recognition is fundamental in biology, underpinning intricate processes through specific protein-ligand interactions. This understanding is pivotal in drug discovery, yet traditional experimental methods face limitations in exploring the vast chemical space. Computational approaches, notably quantitative structure-activity/property relationship analysis, have gained prominence. Molecular fingerprints encode molecular structures and serve as property profiles, which are essential in drug discovery. While two-dimensional (2D) fingerprints are commonly used, three-dimensional (3D) structural interaction fingerprints offer enhanced structural features specific to target proteins. Machine learning models trained on interaction fingerprints enable precise binding prediction. Recent focus has shifted to structure-based predictive modeling, with machine-learning scoring functions excelling due to feature engineering guided by key interactions. Notably, 3D interaction fingerprints are gaining ground due to their robustness. Various structural interaction fingerprints have been developed and used in drug discovery, each with unique capabilities. This review recapitulates the developed structural interaction fingerprints and provides two case studies to illustrate the power of interaction fingerprint-driven machine learning. The first elucidates structure-activity relationships in β2 adrenoceptor ligands, demonstrating the ability to differentiate agonists and antagonists. The second employs a retrosynthesis-based pre-trained molecular representation to predict protein-ligand dissociation rates, offering insights into binding kinetics. Despite remarkable progress, challenges persist in interpreting complex machine learning models built on 3D fingerprints, emphasizing the need for strategies to make predictions interpretable. Binding site plasticity and induced fit effects pose additional complexities. Interaction fingerprints are promising but require continued research to harness their full potential.

Machine learning and traditional QSAR modeling methods: a case study of known PXR activators

Pregnane X receptor (PXR), extensively expressed in human tissues related to digestion and metabolism, is responsible for recognizing and detoxifying diverse xenobiotics encountered by humans. To comprehend the promiscuous nature of PXR and its ability to bind a variety of ligands, computational approaches, viz., quantitative structure-activity relationship (QSAR) models, aid in the rapid dereplication of potential toxicological agents and mitigate the number of animals used to establish a meaningful regulatory decision. Recent advancements in machine learning techniques accommodating larger datasets are expected to aid in developing effective predictive models for complex mixtures (viz., dietary supplements) before undertaking in-depth experiments. Five hundred structurally diverse PXR ligands were used to develop traditional two-dimensional (2D) QSAR, machine-learning-based 2D-QSAR, field-based three-dimensional (3D) QSAR, and machine-learning-based 3D-QSAR models to establish the utility of predictive machine learning methods. Additionally, the applicability domain of the agonists was established to ensure the generation of robust QSAR models. A prediction set of dietary PXR agonists was used to externally-validate generated QSAR models. QSAR data analysis revealed that machine-learning 3D-QSAR techniques were more accurate in predicting the activity of external terpenes with an external validation squared correlation coefficient (R2) of 0.70 versus an R2 of 0.52 in machine-learning 2D-QSAR. Additionally, a visual summary of the binding pocket of PXR was assembled from the field 3D-QSAR models. By developing multiple QSAR models in this study, a robust groundwork for assessing PXR agonism from various chemical backbones has been established in anticipation of the identification of potential causative agents in complex mixtures.

Virtual screening and molecular dynamics simulation approach for the identification of potential multi-target directed ligands for the treatment of Alzheimer's disease

Alzheimer's disease (AD) is a multifactorial neurological disorder characterized by memory loss and cognitive impairment. The currently available single-targeting drugs have miserably failed in the treatment of AD, and multi-target directed ligands (MTDLs) are being explored as an alternative treatment strategy. Cholinesterase and monoamine oxidase enzymes are reported to play a crucial role in the pathology of AD, and multipotent ligands targeting these two enzymes simultaneously are under various phases of design and development. Recent studies have revealed that computational approaches are robust and trusted tools for identifying novel therapeutics. The current research work is focused on the development of potential multi-target directed ligands that simultaneously inhibit acetylcholinesterase (AChE) and monoamine oxidase B (MAO-B) enzymes employing a structure-based virtual screening (SBVS) approach. The ASINEX database was screened after applying pan assay interference and drug-likeness filter to identify novel molecules using three docking precision criteria; High Throughput Virtual Screening (HTVS), Standard Precision (SP), and Extra Precision (XP). Additionally, binding free energy calculations, ADME, and molecular dynamic simulations were employed to get structural insights into the mechanism of protein-ligand binding and pharmacokinetic properties. Three lead molecules viz. AOP19078710, BAS00314308 and BDD26909696 were successfully identified with binding scores of -10.565, -10.543 & -8.066 kcal/mol against AChE and -11.019, -12.357 & -10.068 kcal/mol against MAO-B, better score as compared to the standard inhibitors. In the near future, these molecules will be synthesized and evaluated through in vitro and in vivo assays for their inhibition potential against AChE and MAO-B enzymes.

In-silico guided chemical exploration of KDM4A fragments hits

BACKGROUND

Lysine demethylase enzymes (KDMs) are an emerging class of therapeutic targets, that catalyse the removal of methyl marks from histone lysine residues regulating chromatin structure and gene expression. KDM4A isoform plays an important role in the epigenetic dysregulation in various cancers and is linked to aggressive disease and poor clinical outcomes. Despite several efforts, the KDM4 family lacks successful specific molecular inhibitors.

RESULTS

Herein, starting from a structure-based fragments virtual screening campaign we developed a synergic framework as a guide to rationally design efficient KDM4A inhibitors. Commercial libraries were used to create a fragments collection and perform a virtual screening campaign combining docking and pharmacophore approaches. The most promising compounds were tested in-vitro by a Homogeneous Time-Resolved Fluorescence-based assay developed for identifying selective substrate-competitive inhibitors by means of inhibition of H3K9me3 peptide demethylation. 2-(methylcarbamoyl)isonicotinic acid was identified as a preliminary active fragment, displaying inhibition of KDM4A enzymatic activity. Its chemical exploration was deeply investigated by computational and experimental approaches which allowed a rational fragment growing process. The in-silico studies guided the development of derivatives designed as expansion of the primary fragment hit and provided further knowledge on the structure-activity relationship.

CONCLUSIONS

Our study describes useful insights into key ligand-KDM4A protein interaction and provides structural features for the development of successful selective KDM4A inhibitors.

Harnessing the druggability at orthosteric and allosteric sites of PD-1 for small molecule discovery by an integrated in silico pipeline

The PD-1/PD-L1 interaction is a promising target for small molecule inhibitors in cancer immunotherapy, but targeting this interface has been challenging. While efforts have been made to identify compounds that target the orthosteric sites, no reports have explored the potential of small molecules to target the allosteric region of PD-1. Therefore, our study aims to establish a pipeline to identify small molecules that can effectively bind to either the orthosteric or allosteric pockets of PD-1. We categorized the PD-1 interface into two hot-spot zones (P-and N-zones) based on extensive analysis of its structural, dynamical, and energetic properties. These zones correspond to the orthosteric and allosteric PPI sites, respectively, targeted by monoclonal antibodies. We used a guided virtual screening workflow to identify hits from ∼7 million compounds library, which were then clustered based on structural similarity and assessed by interaction fingerprinting. The selective and diverse chemical representatives were subjected to MD simulations and binding energetics calculations to filter out false positives and identify actual binders. Binding poses metadynamics calculations confirmed the stability of the final hits in the pocket. This study emphasizes the need for an integrated pipeline that uses molecular dynamics simulations and binding energetics to identify potential binders for the dynamic PD-1/PD-L1 interface, due to the lack of small molecule co-crystals. Only a few potential binders were discovered from a large pool of molecules targeting both the allosteric and orthosteric zones. Our results suggest that the allosteric site has more potential than the orthosteric site for inhibitor design. The identified "computational hits" hold potential as starting points for in vitro evaluations followed by hit-to-lead optimization. Overall, this study represents an effort to establish a computational pipeline for exploring and enriching both the allosteric and orthosteric sites of PPI interfaces, "a tough but indispensable nut to crack".

Novel multi-target ligands of dopamine and serotonin receptors for the treatment of schizophrenia based on indazole and piperazine scaffolds-synthesis, biological activity, and structural evaluation

Schizophrenia is a chronic mental disorder that is not satisfactorily treated with available antipsychotics. The presented study focuses on the search for new antipsychotics by optimising the compound D2AAK3, a multi-target ligand of G-protein-coupled receptors (GPCRs), in particular D₂, 5-HT_1A, and 5-HT_2A receptors. Such receptor profile may be beneficial for the treatment of schizophrenia. Compounds 1-16 were designed, synthesised, and subjected to further evaluation. Their affinities for the above-mentioned receptors were assessed in radioligand binding assays and efficacy towards them in functional assays. Compounds 1 and 10, selected based on their receptor profile, were subjected to in vivo tests to evaluate their antipsychotic activity, and effect on memory and anxiety processes. Molecular modelling was performed to investigate the interactions of the studied compounds with D₂, 5-HT_1A, and 5-HT_2A receptors on the molecular level. Finally, X-ray study was conducted for compound 1, which revealed its stable conformation in the solid state.

Molecular dynamics simulation and pharmacokinetics studies of ombuin and quercetin against human pancreatic α-amylase

Diabetes mellitus (DM) is a group of metabolic disorders characterised by chronic hyperglycaemia. DM is currently one of the top ten causes of death in humans. Chronic hyperglycaemia in DM leads to long-term damage and failure of different organs in the body. Type 2 DM (T2D) is the most common DM form, characterised by peripheral insulin resistance, relative insulin deficiency, impaired hepatic glucose production regulation and pancreatic β cell dysfunction. The human pancreatic α-amylase (HPA) inhibitor is currently one of the most effective methods developed to inhibit hyperglycaemia in T2D patients. However, the current standard drug available, acarbose, has been associated with severe side effects following prolonged use in patients. Therefore, an alternative drug capable of effectively inhibiting HPA with minimal side effects is required. Based on our previous study, we further explored the therapeutic potential of quercetin and ombuin via molecular dynamics (MD) simulation. The Desmond Simulation Package was used to run 100-ns MD simulations to examine the steady nature and conformational stability of the ligand-HPA complexes. Post-simulation molecular mechanics-generalised born surface area (MM-GBSA) analysis of HPA's binding free energy with quercetin and ombuin was explored. The lead compounds' drug-likeness, absorption, distribution, metabolism and elimination properties were also studied using the SwissADME tool. These results indicate that quercetin and ombuin have great potential as anti-DM drugs with more favourable properties than acarbose.Communicated by Ramaswamy H. Sarma.

Structure-Based Discovery of Mouse Trace Amine-Associated Receptor 5 Antagonists

Trace amine-associated receptors (TAARs) were discovered in 2001 as new members of class A G protein-coupled receptors (GPCRs). With the only exception of TAAR1, TAAR members (TAAR2-9, also known as noncanonical olfactory receptors) were originally described exclusively in the olfactory epithelium and believed to mediate the innate perception of volatile amines. However, most noncanonical olfactory receptors are still orphan receptors. Given its recently discovered nonolfactory expression and therapeutic potential, TAAR5 has been the focus of deorphanization campaigns that led to the discovery of a few druglike antagonists. Here, we report four novel TAAR5 antagonists identified through high-throughput screening, which, along with the four ligands published in the literature, constituted our starting point to design a computational strategy for the identification of TAAR5 ligands. We developed a structure-based virtual screening protocol that allowed us to identify three new TAAR5 antagonists with a hit rate of 10%. Despite lacking an experimental structure, we accurately modeled the TAAR5 binding site by integrating comparative sequence- and structure-based analyses of serotonin receptors with homology modeling and side-chain optimization. In summary, we have identified seven new TAAR5 antagonists that could serve as lead candidates for the development of new treatments for depression, anxiety, and neurodegenerative diseases.

Exploring the potential of machine learning to design antidiabetic molecules: a comprehensive study with experimental validation

Recent advances in hardware and software algorithms have led to the rise of data-driven approaches for designing therapeutic modalities. One of the major causes of human mortality is diabetes. Thus, there is a tremendous opportunity for research into effective antidiabetic designs. Therefore, in this study, we used machine learning-based small molecule design. We used various chemoinformatic and binary fingerprint techniques on small molecules to construct multiple models for alpha-amylase inhibitors. Among these models, the top models were used for ensemble-based machine learning predictions on libraries of organic molecules supplemented with synthetic scaffolds that could be used as antidiabetic agents. Further, involved identifying 10 promising molecules from computational studies and determining their inhibitory effects on alpha-amylase. These molecules were synthesised and thoroughly analysed to assess their biological inhibitory properties. Then, thermodynamic simulations were conducted to determine the stability and affinity of experimentally active molecules. The research results showcased the top 10 ML models recorded impressive statistics with an average model score of 0.8216, Pearson-r value of 0.827 and external validation yielding a Q2 value of 0.835, proving their reliability and accuracy. Ten derivatives of benzothiophene dioxolane was prime research focus due to computational predictions. The biological inhibitory assay of synthesised molecules showed that small molecules with ID ALC5 and ALC6 exhibited inhibitory efficiencies (IC50) of 2.1 ± 0.14 µM and 5.71 ± 0.02 µM against alpha-amylase enzyme, whereas other molecules showed moderate inhibition. In conclusion, the positive results of the experiment indicate that researchers should explore machine learning-driven design.Communicated by Ramaswamy H. Sarma.

Discovery of 2-(Methylcarbonylamino) thiazole as PDE4 inhibitors via virtual screening and biological evaluation

Phosphodiesterase-4, the primary enzyme responsible for cAMP degradation in the majority of immune and inflammatory cells, plays a critical role in the regulation of intracellular cAMP levels. Consequently, small molecular entities capable of inhibiting PDE4 have been employed in the treatment of inflammation-associated disorders, such as chronic obstructive pulmonary disease (COPD), psoriasis, atopic dermatitis (AD), inflammatory bowel diseases (IBD), rheumatic arthritis (RA). In the present investigation, a multi-faceted approach was employed to identify novel PDE4 inhibitors, utilizing the co-crystallization structure of PDE4B available in the Protein Data Bank (PDB) database, drug-like screening, false positive filtration, similarity and ADMET screen, as well as molecular docking via multiple software platforms, in conjunction with bioactivity assays. A thiazol-3-propanamides derivative, designated MR9, was discovered to inhibit PDE4B activity with IC50 values of 2.12 μM and suppress cellular inflammatory factor TNF-α release with an EC50 value of 3.587 μM. These findings suggest that the innovative active scaffold of MR9 offers a promising foundation for further structural refinement aimed at developing more potent PDE4 inhibitors.

Discovery of novel cGAS inhibitors based on natural flavonoids

Cyclic GMP-AMP synthase (cGAS) plays an important role in the inflammatory response. It has been reported that aberrant activation of cGAS is associated with a variety of immune-mediated inflammatory disorders. The development of small molecule inhibitors of cGAS has been considered as a promising therapeutic strategy for the diseases. Flavonoids, a typical class of natural products, are known for their anti-inflammatory activities. Although cGAS is closely associated with inflammation, the potential effects of natural flavonoid compounds on cGAS have been rarely studied. Therefore, we screened an in-house natural flavonoid library by pyrophosphatase (PPiase) coupling assay and identified novel cGAS inhibitors baicalein and baicalin. Subsequently, crystal structures of the two natural flavonoids in complex with human cGAS were determined, which provide mechanistic insight into the anti-inflammatory activities of baicalein and baicalin at the molecular level. After that, a virtual screening based on the crystal structures of baicalein and baicalin in complex with human cGAS was performed. As a result, compound C20 was identified to inhibit both human and mouse cGAS with IC50 values of 2.28 and 1.44 μM, respectively, and its detailed interactions with human cGAS were further revealed by the X-ray crystal structure determination. These results demonstrate the potential of natural products used as hits in drug discovery and provide valuable hints for further development of cGAS inhibitors.

Multitask joint learning with graph autoencoders for predicting potential MiRNA-drug associations

The occurrence of many diseases is associated with miRNA abnormalities. Predicting potential drug-miRNA associations is of great importance for both disease treatment and new drug discovery. Most computation-based approaches learn one task at a time, ignoring the information contained in other tasks in the same domain. Multitask learning can effectively enhance the prediction performance of a single task by extending the valid information of related tasks. In this paper, we presented a multitask joint learning framework (MTJL) with a graph autoencoder for predicting the associations between drugs and miRNAs. First, we combined multiple pieces of information to construct a high-quality similarity network of both drugs and miRNAs and then used a graph autoencoder (GAE) to learn their embedding representations separately. Second, to further improve the embedding quality of drugs, we added an auxiliary task to classify drugs using the learned representations. Finally, the embedding representations of drugs and miRNAs were linearly transformed to obtain the predictive association scores between them. A comparison with other state-of-the-art models shows that MTJL has the best prediction performance, and ablation experiments show that the auxiliary task can enhance the embedding quality and improve the robustness of the model. In addition, we show that MTJL has high utility in predicting potential associations between drugs and miRNAs by conducting two case studies.

Utilizing Heteroatom Types and Numbers from Extensive Ligand Libraries to Develop Novel hERG Blocker QSAR Models Using Machine Learning-Based Classifiers

The human ether-à-go-go-related gene (hERG) channel plays a crucial role in membrane repolarization. Any disruptions in its function can lead to severe cardiovascular disorders such as long QT syndrome (LQTS), which increases the risk of serious cardiovascular problems such as tachyarrhythmia and sudden cardiac death. Drug-induced LQTS is a significant concern and has resulted in drug withdrawals from the market in the past. The main objective of this study is to pinpoint crucial heteroatoms present in ligands that initiate interactions leading to the effective blocking of the hERG channel. To achieve this aim, ligand-based quantitative structure-activity relationships (QSAR) models were constructed using extensive ligand libraries, considering the heteroatom types and numbers, and their associated hERG channel blockage pIC50 values. Machine learning-assisted QSAR models were developed to analyze the key structural components influencing compound activity. Among the various methods, the KPLS method proved to be the most efficient, allowing the construction of models based on eight distinct fingerprints. The study delved into investigating the influence of heteroatoms on the activity of hERG blockers, revealing their significant role. Furthermore, by quantifying the effect of heteroatom types and numbers on ligand activity at the hERG channel, six compound pairs were selected for molecular docking. Subsequent molecular dynamics simulations and per residue MM/GBSA calculations were performed to comprehensively analyze the interactions of the selected pair compounds.

Discovery of Hit Compounds Targeting the P4 Allosteric Site of K-RAS, Identified through Ensemble-Based Virtual Screening

Mutants of Ras are oncogenic drivers of a large number of human tumors. Despite being recognized as an attractive target for the treatment of cancer, the high affinity for its substrate tagged the protein as undruggable for a few years. The identification of cryptic pockets on the protein surface gave the opportunity to identify molecules capable of acting as allosteric modulators. Several molecules were disclosed in recent years, with sotorasib and adagrasib already approved for clinical use. The present study makes use of computational methods to characterize eight prospective allosteric pockets (P1-P8) in K-Ras, four of which (P1-P4) were previously characterized in the literature. The present study also describes the results of a virtual screening study focused on the discovery of hit compounds, binders of the P4 site that can be considered as peptidomimetics of a fragment of the SOS αI helix, a guanine exchange factor of Ras. After a detailed description of the computational procedure followed, we disclose five hit compounds, prospective binders of the P4 allosteric site that exhibit an inhibitory capability higher than 30% in a cell proliferation assay at 50 μM.

Synthesis and Biological Evaluation of Novel Uracil Derivatives as Thymidylate Synthase Inhibitors

Cell division is driven by nucleic acid metabolism, and thymidylate synthase (TYMS) catalyzes a rate-limiting step in nucleotide synthesis. As a result, thymidylate synthase has emerged as a critical target in chemotherapy. 5-Fluorouracil (5-FU) is currently being used to treat a wide range of cancers, including breast, pancreatic, head and neck, colorectal, ovarian, and gastric cancers The objective of this study was to establish a new methodology for the low-cost, one-pot synthesis of uracil derivatives (UD-1 to UD-5) and to evaluate their therapeutic potential in BC cells. One-pot organic synthesis processes using a single solvent were used for the synthesis of drug analogues of Uracil. Integrated bioinformatics using GEPIA2, UALCAN, and KM plotter were utilized to study the expression pattern and prognostic significance of TYMS, the key target gene of 5-fluorouracil in breast cancer patients. Cell viability, cell proliferation, and colony formation assays were used as in vitro methods to validate the in silico lead obtained. BC patients showed high levels of thymidylate synthase, and high expression of thymidylate synthase was found associated with poor prognosis. In silico studies indicated that synthesized uracil derivatives have a high affinity for thymidylate synthase. Notably, the uracil derivatives dramatically inhibited the proliferation and colonization potential of BC cells in vitro. In conclusion, our study identified novel uracil derivatives as promising therapeutic options for breast cancer patients expressing the augmented levels of thymidylate synthase.

Dual inhibition of cyclooxygenase-2 (COX-2) and 5-lipoxygenase (5-LOX) by resveratrol derivatives in cancer therapy: in silico approach

In a number of human cancers, both cycloxygenase-2 (COX-2) and 5-lipoxygenase (5-LOX) are up-regulated and co-expressed, promoting cancer cell proliferation and angiogenesis. Resveratrol (3,4',5-trihydroxy-trans-stilbene) is a natural polyphenolic phytoalexin found in a variety of plants that influences various signal-transduction pathways which control apoptosis, cell growth and cell division, metastasis, angiogenesis and inflammation, and has an impact on cancer stages ranging from initiation to progression. In this work, molecular docking and molecular dynamics simulation method are employed to design resveratrol derivatives for COX-2 and 5-LOX enzymes. By attaching several functional groups on four different places of the resveratrol scaffold, the R group enumeration approach was employed to build four libraries of resveratrol derivatives. Thus, R group enumeration is done to focus on the enhancement of potency of compounds and other chemical characteristics like solubility. Drug-like filters such as REOS 1, 2, 3 and PAINS were applied to the libraries, generating a total of 5557 compounds. Drug-like filters such as REOS and PAINS-1, 2 and 3 were applied to the libraries, generating a total of 5557 compounds. All of these compounds were docked with both enzymes using the Glide SP and XP docking methods. Enrichment calculations were performed using 40 compounds from XP docking along with resveratrol, and 1000 decoy compounds from the DUD-E database to validate the docking protocol. The stability of the complexes was further studied using molecular dynamics simulation, radius of gyration, MM/GBSA, H bond monitoring and electrostatic potential surface (EPS). ADMET properties of compounds were studied using SwissADME and pkCSM server.Communicated by Ramaswamy H. Sarma.

Virtual screening and biological evaluation to identify pharmaceuticals potentially causing hypertension and hypokalemia by inhibiting steroid 11β-hydroxylase

Several drugs were found after their market approval to unexpectedly inhibit adrenal 11β-hydroxylase (CYP11B1)-dependent cortisol synthesis. Known side-effects of CYP11B1 inhibition include hypertension and hypokalemia, due to a feedback activation of adrenal steroidogenesis, leading to supraphysiological concentrations of 11-deoxycortisol and 11-deoxycorticosterone that can activate the mineralocorticoid receptor. This results in potassium excretion and sodium and water retention, ultimately causing hypertension. With the risk known but usually not addressed in preclinical evaluation, this study aimed to identify drugs and drug candidates inhibiting CYP11B1. Two conceptually different virtual screening methods were combined, a pharmacophore based and an induced fit docking approach. Cell-free and cell-based CYP11B1 activity measurements revealed several inhibitors with IC50 values in the nanomolar range. Inhibitors include retinoic acid metabolism blocking agents (RAMBAs), azole antifungals, α2-adrenoceptor ligands, and a farnesyltransferase inhibitor. The active compounds share a nitrogen atom embedded in an aromatic ring system. Structure activity analysis identified the free electron pair of the nitrogen atom as a prerequisite for the drug-enzyme interaction, with its pKa value as an indicator of inhibitory potency. Another important parameter is drug lipophilicity, exemplified by etomidate. Changing its ethyl ester moiety to a more hydrophilic carboxylic acid group dramatically decreased the inhibitory potential, most likely due to less efficient cellular uptake. The presented work successfully combined different in silico and in vitro methods to identify several previously unknown CYP11B1 inhibitors. This workflow facilitates the identification of compounds that inhibit CYP11B1 and therefore pose a risk for inducing hypertension and hypokalemia.

Flavonoids as P-glycoprotein inhibitors for multidrug resistance in cancer: an in-silico approach

Cancer has become a leading cause of mortality due to non-communicable diseases after cardiovascular disease worldwide and is increasing day by day at a daunting pace. According to an estimate by 2040 there will be 28.4 million cancer cases. Occurrence of multidrug resistance has further worsened the scenario of available cancer treatment. Among different mechanisms of multidrug resistance efflux of xenobiotics by ABC transporter is of prime importance. P-glycoprotein (P-gp) is the major factor behind occurrence of multidrug resistance due to its wide distribution and invariably big binding cavity. Various generations of chemical inhibitors for P-gp have been designed and tested are not devoid of major side effects. Thus, in present study flavonoids a major class of natural compounds was virtually screened in order to find molecules which can be used as selective P-gp inhibitors to be used along with chemotherapeutics. After screening 4275 molecules from different classes of flavonoids i.e. flavan, flavanol, flavonone, flavone, anthocyanins, and isoflavone, through Glide docking top ten hit molecules were selected based on their binding affinity, binding energy calculation and pharmacokinetic properties. All the hit molecules were found to have docking score within the range of -11.202 to -9.699 kcal/mol showing very strong interaction with the amino acid residues of binding pocket. Whereas, dock score of standard P-gp inhibitor verapamil was -4.984 kcal/mol. The ligand and protein complex were found to be quite stable while run through molecular dynamics simulations.Communicated by Ramaswamy H. Sarma.

Integrated approaches for the recognition of small molecule inhibitors for Toll-like receptor 4

Toll-like receptors (TLRs) are pattern recognition receptors present on the surface of cells playing a crucial role in innate immunity. One of the TLRs, TLR4, recognizes LPS (Lipopolysaccharide) as its ligand leading to the release of anti-inflammatory mediators as well as pro-inflammatory cytokines through signal transduction and domain recruitment. TLR4 homodimerizes at its intracellular TIR (Toll/interleukin-1 receptor) domain that helps in the recruitment of the TRAM/TICAM2 (TIR domain-containing adaptor molecule 2) molecule. TRAM also contains TIR domain which in turn, dimerizes and functions as an adapter protein to further recruit TRIF/TICAM1 (TIR domain-containing adaptor molecule 1) protein for mediating downstream signaling. Apart from LPS, TLR4 also recognizes endogenous ligands like fibrinogen, HMGB1, and hyaluronan in autoimmune conditions and sepsis. We employed computational approaches to target TRAM and recognize small molecule inhibitors from small molecules of natural origin, as contained in the Super Natural II database. Finally, cell reporter assays and NMR studies enabled the identification of promising lead compounds. Hence, this study aims to attenuate the signaling of the TLR4-TRAM-TRIF cascade in these auto-inflammatory conditions.

The occurrence, tissue distribution, and PBT potential of per- and polyfluoroalkyl substances in the freshwater organisms from the Yangtze river via nontarget analysis

Numerous emerging per- and polyfluoroalkyl substances (PFASs) occur in the aquatic environment, posing a threat to aquatic ecosystems and human health. In this study, we conducted a nontarget analysis on 3 surface water samples and 92 tissue samples of 16 fish collected from the Yangtze River to investigate the patterns, tissue distribution, and environmental impacts of emerging PFASs. A total of 43 PFASs from 11 classes were identified, including 17 legacy PFASs and 26 emerging PFASs. Among the 43 PFASs, seven PFASs were reported in biota for the first time while five PFASs were reported in the environment for the first time. Chlorine substituted perfluoroalyl ether sulfonic acids were the major emerging PFASs detected in organisms. Our results showed that most emerging PFASs tended to accumulate in the liver whereas perfluorinated sulfonamides tended to accumulate in the blood, and all of the emerging PFASs accumulated less in the muscle. Methods for evaluating the persistence, bioaccumulation, and toxicity (PBT) of PFASs were developed by combining the in-silico methods and experimental methods. Long-chain PFASs were found to have extremely high PBT scores compared to short-chain PFASs. Additionally, most emerging PFASs exhibited comparable PBT characteristics with legacy PFASs, especially Cl-substituted PFASs.

Unbinding Kinetics of Muscarinic M3 Receptor Antagonists Explained by Metadynamics Simulations

The residence time (RT), the time for which a drug remains bound to its biological target, is a critical parameter for drug design. The prediction of this key kinetic property has been proven to be challenging and computationally demanding in the framework of atomistic simulations. In the present work, we setup and applied two distinct metadynamics protocols to estimate the RTs of muscarinic M3 receptor antagonists. In the first method, derived from the conformational flooding approach, the kinetics of unbinding is retrieved from a physics-based parameter known as the acceleration factor α (i.e., the running average over time of the potential deposited in the bound state). Such an approach is expected to recover the absolute RT value for a compound of interest. In the second method, known as the tMETA-D approach, a qualitative estimation of the RT is given by the time of simulation required to drive the ligand from the binding site to the solvent bulk. This approach has been developed to reproduce the change of experimental RTs for compounds targeting the same target. Our analysis shows that both computational protocols are able to rank compounds in agreement with their experimental RTs. Quantitative structure-kinetics relationship (SKR) models can be identified and employed to predict the impact of a chemical modification on the experimental RT once a calibration study has been performed.

Virtual Screening Strategy to Identify Retinoic Acid-Related Orphan Receptor γt Modulators

Molecular docking is a key method used in virtual screening (VS) campaigns to identify small-molecule ligands for drug discovery targets. While docking provides a tangible way to understand and predict the protein-ligand complex formation, the docking algorithms are often unable to separate active ligands from inactive molecules in practical VS usage. Here, a novel docking and shape-focused pharmacophore VS protocol is demonstrated for facilitating effective hit discovery using retinoic acid receptor-related orphan receptor gamma t (RORγt) as a case study. RORγt is a prospective target for treating inflammatory diseases such as psoriasis and multiple sclerosis. First, a commercial molecular database was flexibly docked. Second, the alternative docking poses were rescored against the shape/electrostatic potential of negative image-based (NIB) models that mirror the target's binding cavity. The compositions of the NIB models were optimized via iterative trimming and benchmarking using a greedy search-driven algorithm or brute force NIB optimization. Third, a pharmacophore point-based filtering was performed to focus the hit identification on the known RORγt activity hotspots. Fourth, free energy binding affinity evaluation was performed on the remaining molecules. Finally, twenty-eight compounds were selected for in vitro testing and eight compounds were determined to be low μM range RORγt inhibitors, thereby showing that the introduced VS protocol generated an effective hit rate of ~29%.

Inhibiting a promiscuous GPCR: iterative discovery of bitter taste receptor ligands

The human GPCR family comprises circa 800 members, activated by hundreds of thousands of compounds. Bitter taste receptors, TAS2Rs, constitute a large and distinct subfamily, expressed orally and extra-orally and involved in physiological and pathological conditions. TAS2R14 is the most promiscuous member, with over 150 agonists and 3 antagonists known prior to this study. Due to the scarcity of inhibitors and to the importance of chemical probes for exploring TAS2R14 functions, we aimed to discover new ligands for this receptor, with emphasis on antagonists. To cope with the lack of experimental structure of the receptor, we used a mixed experimental/computational methodology which iteratively improved the performance of the predicted structure. The increasing number of active compounds, obtained here through experimental screening of FDA-approved drug library, and through chemically synthesized flufenamic acid derivatives, enabled the refinement of the binding pocket, which in turn improved the structure-based virtual screening reliability. This mixed approach led to the identification of 10 new antagonists and 200 new agonists of TAS2R14, illustrating the untapped potential of rigorous medicinal chemistry for TAS2Rs. 9% of the ~ 1800 pharmaceutical drugs here tested activate TAS2R14, nine of them at sub-micromolar concentrations. The iterative framework suggested residues involved in the activation process, is suitable for expanding bitter and bitter-masking chemical space, and is applicable to other promiscuous GPCRs lacking experimental structures.

Novel Thiosemicarbazone Quantum Dots in the Treatment of Alzheimer's Disease Combining In Silico Models Using Fingerprints and Physicochemical Descriptors

Searching for thiosemicarbazone derivatives with the potential to inhibit acetylcholinesterase for the treatment of Alzheimer's disease (AD) is an important current goal. The QSAR_KPLS, QSAR_ANN, and QSAR_SVR models were constructed using binary fingerprints and physicochemical (PC) descriptors of 129 thiosemicarbazone compounds screened from a database of 3791 derivatives. The R ² and Q ² values for the QSAR_KPLS, QSAR_ANN, and QSAR_SVR models are greater than 0.925 and 0.713 using dendritic fingerprint (DF) and PC descriptors, respectively. The in vitro pIC₅₀ activities of four new design-oriented compounds N1, N2, N3, and N4, from the QSAR_KPLS model using DFs, are consistent with the experimental results and those from the QSAR_ANN and QSAR_SVR models. The designed compounds N1, N2, N3, and N4 do not violate Lipinski-5 and Veber rules using the ADME and BoiLED-Egg methods. The binding energy, kcal mol^-1, of the novel compounds to the 1ACJ-PDB protein receptor of the AChE enzyme was also obtained by molecular docking and dynamics simulations consistent with those predicted from the QSAR_ANN and QSAR_SVR models. New compounds N1, N2, N3, and N4 were synthesized, and the experimental in vitro pIC₅₀ activity was determined in agreement with those obtained from in silico models. The newly synthesized thiosemicarbazones N1, N2, N3, and N4 can inhibit 1ACJ-PDB, which is predicted to be able to cross the barrier. The DFT B3LYP/def-SV(P)-ECP quantization calculation method was used to calculate E _HOMO and E _LUMO to account for the activities of compounds N1, N2, N3, and N4. The quantum calculation results explained are consistent with those obtained in in silico models. The successful results here may contribute to the search for new drugs for the treatment of AD.

Exploration of LPS2 agonist binding modes using the combination of a new hydrophobic scaffold and homology modeling

Lysophosphatidylserine (LysoPS) is an endogenous pan-agonist of three G-protein coupled receptors (GPCRs): LPS₁/GPR34, LPS₂/P2Y₁₀, and LPS₃/GPR174, and we previously reported a series of LysoPS-based agonists of these receptors. Interestingly, we found that LPS₁ agonist activity was very sensitive to structural change at the hydrophobic fatty acid moiety, whereas LPS₂ agonist activity was not. Here, to probe the molecular basis of LPS₂ agonist binding, we developed a new class of hydrophobic fatty acid surrogates having a biphenyl-ether scaffold. The LPS₂ agonist activity of these compounds proved sensitive to molecular modification of the hydrophobic skeleton. Thus, we next constructed an LPS₂ model by homology modeling and docking/molecular dynamics (MD) simulation, and validated it by means of SAR studies together with point mutations of selected receptor amino-acid residues. The putative ligand-binding site of LPS₂ is Γ-shaped, with a hydrophilic site horizontally embedded in the receptor transmembrane helix bundles and a perpendicular hydrophobic groove adjoining transmembrane domains 4 and 5 that is open to the membrane bilayer. The binding poses of LPS₂ agonists to this site are consistent with easy incorporation of various kinds of fatty acid surrogates. Structural development based on this model afforded a series of potent and selective LPS₂ full agonists, which showed enhanced in vitro actin stress fiber formation effect.

Rational Computational Approaches in Drug Discovery: Potential Inhibitors for Allosteric Regulation of Mutant Isocitrate Dehydrogenase-1 Enzyme in Cancers

Mutations in homodimeric isocitrate dehydrogenase (IDH) enzymes at specific arginine residues result in the abnormal activity to overproduce D-2 hydroxyglutarate (D-2HG), which is often projected as solid oncometabolite in cancers and other disorders. As a result, depicting the potential inhibitor for D-2HG formation in mutant IDH enzymes is a challenging task in cancer research. The mutation in the cytosolic IDH1 enzyme at R132H, especially, may be associated with higher frequency of all types of cancers. So, the present work specifically focuses on the design and screening of allosteric site binders to the cytosolic mutant IDH1 enzyme. The 62 reported drug molecules were screened along with biological activity to identify the small molecular inhibitors using computer-aided drug design strategies. The designed molecules proposed in this work show better binding affinity, biological activity, bioavailability, and potency toward the inhibition of D-2HG formation compare to the reported drugs in the in silico approach.

8-Hydroxyquinoline derivatives suppress GLI1-mediated transcription through multiple mechanisms

Aberrant activation of the Hedgehog (Hh) signaling pathway has been observed in various human malignancies. Glioma-associated oncogene transcription factor 1 (GLI1) is the ultimate effector of the canonical Hh pathway and has also been identified as a common regulator of several tumorigenic pathways prevalent in Hh-independent cancers. The anti-cancer potential of GLI1 antagonism with small molecule inhibitors has demonstrated initial promise; however, the continued development of GLI1 inhibitors is still needed. We previously identified a scaffold containing an 8-hydroxyquinoline as a promising lead GLI1 inhibitor (compound 1). To further develop this scaffold, we performed a systematic structure-activity relationship study to map the structural requirements of GLI1 inhibition by this chemotype. A series of biophysical and cellular experiments identified compound 39 as an enhanced GLI1 inhibitor with improved activity. In addition, our studies on this scaffold suggest a potential role for SRC family kinases in regulating oncogenic GLI1 transcriptional activity.

Assessing financial factors for oil supply disruptions and its impact on oil supply security and transportation risks

The evaluation of energy security offers a standard for policy research and highlights the problems of securing the energy supply. A composite index for analyzing the risk of Southeast Asian nations' oil supply is developed in this study. Indicators used to calculate the index include the import-to-LGE ratio, GPR, market liquidity, gross domestic product, the import-to-consumption ratio, heterogeneity, oil price volatility, US$ volatility, and transportation risk. The index is based on these and other factors. According to the findings, Nepal and Sri Lanka are the most susceptible to oil supply interruptions. This indicates that India is more likely to shift its oil suppliers. At the same time, Maldives, Nepal, and Sri Lanka have the lowest supply risk scores, indicating that they are the most vulnerable to supply disruptions. Reduce the effect of oil supply risk by enacting policies such as the adoption of renewable technologies, nuclear power generation, diversification of exporting supplies, and reducing fossil fuel subsidies.

In silico discovery of potent inhibitors against monkeypox's major structural proteins

Monkeypox virus (MPXV) outbreak in non-endemic countries is a worldwide public health emergency. An enveloped double-stranded DNA virus belongs to the genus Orth poxvirus. A viral zoonotic infection known as monkeypox has been a serious risk to public health, especially in Africa. However, it has recently spread to other continents, so it might soon become a worldwide problem. There is an increased risk of transmission of the virus because there is a lack of effective treatment that cures the disease. To stop the multi-country outbreak from spreading, it is important to discover effective medications urgently. The objective of the current study is to swiftly find new treatments for the monkeypox virus using advanced computational approaches. By investigating five potential MPXV targets (DNA ligase, Palmytilated Extracellular Enveloped Virus (EEV) membrane protein, Scaffold protein D13, Thymidylate Kinase, and Viral core cysteine proteinase), this research was carried out using cutting-edge computational techniques against human monkeypox virus infection. Here we present the accurate 3D structures and their binding cavities of the selected targets with higher confidence using AlphaFold 2 and SiteMap analysis. Molecular docking and MD simulation analysis revealed the top five potential lead compounds with higher binding affinity and stability toward selected targets. Binding free energy calculations and other essential dynamics analysis supports the finding. The selected lead compounds utilizing virtual screening and drug repurposing approach reported in this study are beneficial for medical scientists and experimental biologists in drug development for the treatment of human MPXV.Communicated by Ramaswamy H. Sarma.

Discovery of Small-Molecule Autophagy Inhibitors by Disrupting the Protein-Protein Interactions Involving Autophagy-Related 5

One possible strategy for modulating autophagy is to disrupt the critical protein-protein interactions (PPIs) formed during this process. Our attention is on the autophagy-related 12 (ATG12)-autophagy-related 5 (ATG5)-autophagy-related 16-like 1 (ATG16L1) heterotrimer complex, which is responsible for ATG8 translocation from ATG3 to phosphatidylethanolamine. In this work, we discovered a compound with an (E)-3-(2-furanylmethylene)-2-pyrrolidinone core moiety (T1742) that blocked the ATG5-ATG16L1 and ATG5-TECAIR interactions in the in vitro binding assay (IC₅₀ = 1-2 μM) and also exhibited autophagy inhibition in cellular assays. The possible binding mode of T1742 to ATG5 was predicted through molecular modeling, and a batch of derivatives sharing essentially the same core moiety were synthesized and tested. The outcomes of the in vitro binding assay and the flow cytometry assay of those newly synthesized compounds were generally consistent. This work has validated our central hypothesis that small-molecule inhibitors of the PPIs involving ATG5 can tune down autophagy effectively, and their pharmaceutical potential may be further explored.

Chemical Proteomics with Novel Fully Functionalized Fragments and Stringent Target Prioritization Identifies the Glutathione-Dependent Isomerase GSTZ1 as a Lung Cancer Target

Photoreactive fragment-like probes have been applied to discover target proteins that constitute novel cellular vulnerabilities and to identify viable chemical hits for drug discovery. Through forming covalent bonds, functionalized probes can achieve stronger target engagement and require less effort for on-target mechanism validation. However, the design of probe libraries, which directly affects the biological target space that is interrogated, and effective target prioritization remain critical challenges of such a chemical proteomic platform. In this study, we designed and synthesized a diverse panel of 20 fragment-based probes containing natural product-based privileged structural motifs for small-molecule lead discovery. These probes were fully functionalized with orthogonal diazirine and alkyne moieties and used for protein crosslinking in live lung cancer cells, target enrichment via "click chemistry," and subsequent target identification through label-free quantitative liquid chromatography-tandem mass spectrometry analysis. Pair-wise comparison with a blunted negative control probe and stringent prioritization via individual cross-comparisons against the entire panel identified glutathione S-transferase zeta 1 (GSTZ1) as a specific and unique target candidate. DepMap database query, RNA interference-based gene silencing, and proteome-wide tyrosine reactivity profiling suggested that GSTZ1 cooperated with different oncogenic alterations by supporting survival signaling in refractory non-small cell lung cancer cells. This finding may form the basis for developing novel GSTZ1 inhibitors to improve the therapeutic efficacy of oncogene-directed targeted drugs. In summary, we designed a novel fragment-based probe panel and developed a target prioritization scheme with improved stringency, which allows for the identification of unique target candidates, such as GSTZ1 in refractory lung cancer.

Computer-aided identification, synthesis, and biological evaluation of DNA polymerase η inhibitors for the treatment of cancer

In cancer cells, Pol η allows DNA replication and cell proliferation even in the presence of chemotherapeutic drug-induced damages, like in the case of platinum-containing drugs. Inhibition of Pol η thus represents a promising strategy to overcome drug resistance and preserve the effectiveness of chemotherapeutic drugs. Here, we report the discovery of a novel class of Pol ƞ inhibitors, with 35 active close analogs. Compound 21 (ARN24964) stands out as the best inhibitor, with an IC₅₀ value of 14.7 μM against Pol η and a good antiproliferative activity when used in combination with cisplatin - with a synergistic effect in three different cancer cell lines (A375, A549, OVCAR3). Moreover, it is characterized by a favorable drug-like profile in terms of its aqueous kinetic solubility, plasma and metabolic stability. Thus, ARN24964 is a promising compound for further structure-based drug design efforts toward developing drugs to solve or limit the issue of drug resistance to platinum-containing drugs in cancer patients.

Predict Ionization Energy of Molecules Using Conventional and Graph-Based Machine Learning Models

Ionization energy (IE) is an important property of molecules. It is highly desirable to predict IE efficiently based on, for example, machine learning (ML)-powered quantitative structure-property relationships (QSPR). In this study, we systematically compare the performance of different machine learning models in predicting the IE of molecules with distinct functional groups obtained from the NIST webbook. Mordred and PaDEL are used to generate informative and computationally inexpensive descriptors for conventional ML models. Using a descriptor to indicate if the molecule is a radical can significantly improve the performance of these ML models. Support vector regression (SVR) is the best conventional ML model for IE prediction. In graph-based models, the AttentiveFP gives an even better performance compared to SVR. The difference between these two types of models mainly comes from their predictions for radical molecules, where the local environment around an unpaired electron is better described by graph-based models. These results provide not only high-performance models for IE prediction but also useful information in choosing models to obtain reliable QSPR.