Role of computer-aided drug design in modern drug discovery

Targeting bacterial growth in biofilm conditions: rational design of novel inhibitors to mitigate clinical and food contamination using QSAR

Antimicrobial resistance (AMR) is a pressing global issue exacerbated by the abuse of antibiotics and the formation of bacterial biofilms, which cause up to 80% of human bacterial infections. This study presents a computational strategy to address AMR by developing three novel quantitative structure-activity relationship (QSAR) models based on molecular topology to identify potential anti-biofilm and antibacterial agents. The models aim to determine the chemo-topological pattern of Gram (+) antibacterial, Gram (-) antibacterial, and biofilm formation inhibition activity. The models were applied to the virtual screening of a commercial chemical database, resulting in the selection of 58 compounds. Subsequent in vitro assays showed that three of these compounds exhibited the most promising antibacterial activity, with potential applications in enhancing food and medical device safety.

Novel dual-targeting inhibitors of NSD2 and HDAC2 for the treatment of liver cancer: structure-based virtual screening, molecular dynamics simulation, and in vitro and in vivo biological activity evaluations

Liver cancer exhibits a high degree of heterogeneity and involves intricate mechanisms. Recent research has revealed the significant role of histone lysine methylation and acetylation in the epigenetic regulation of liver cancer development. In this study, five inhibitors capable of targeting both histone lysine methyltransferase nuclear receptor-binding SET domain 2 (NSD2) and histone deacetylase 2 (HDAC2) were identified using a structure-based virtual screening approach. Notably, DT-NH-1 displayed a potent inhibition of NSD2 (IC50 = 0.08 ± 0.03 μM) and HDAC2 (IC50 = 5.24 ± 0.87 nM). DT-NH-1 also demonstrated a strong anti-proliferative activity against various liver cancer cell lines, particularly HepG2 cells, and exhibited a high level of biological safety. In an experimental xenograft model involving HepG2 cells, DT-NH-1 showed a significant reduction in tumour growth. Consequently, these findings indicate that DT-NH-1 will be a promising lead compound for the treatment of liver cancer with epigenetic dual-target inhibitors.

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

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

Comprehensive detection and characterization of human druggable pockets through binding site descriptors

Druggable pockets are protein regions that have the ability to bind organic small molecules, and their characterization is essential in target-based drug discovery. However, deriving pocket descriptors is challenging and existing strategies are often limited in applicability. We introduce PocketVec, an approach to generate pocket descriptors via inverse virtual screening of lead-like molecules. PocketVec performs comparably to leading methodologies while addressing key limitations. Additionally, we systematically search for druggable pockets in the human proteome, using experimentally determined structures and AlphaFold2 models, identifying over 32,000 binding sites across 20,000 protein domains. We then generate PocketVec descriptors for each site and conduct an extensive similarity search, exploring over 1.2 billion pairwise comparisons. Our results reveal druggable pocket similarities not detected by structure- or sequence-based methods, uncovering clusters of similar pockets in proteins lacking crystallized inhibitors and opening the door to strategies for prioritizing chemical probe development to explore the druggable space.

Chalcone-based benzenesulfonamides as potent and selective inhibitors for human carbonic anhydrase II: Design, synthesis, in vitro, and in silico studies

Sulfonamides are promising classical carbonic anhydrase (CA; EC 4.2.1.1) inhibitors, being used for several medical purposes such as diuretics, anticonvulsants, topically acting antiglaucoma agents, for antiobesity and anticancer therapies. Herein, a series of chalcone-based benzenesulfonamides (3a‒m) was synthesized and assessed for its inhibitory activity against a panel of four human carbonic anhydrases (hCA isoforms I, II, IX, and XII). Most compounds displayed single- to double-digit nanomolar inhibition constants (Kis), with some derivatives being more potent and/or selective than the standard drug acetazolamide (AAZ). Among the synthesized compounds, 3g compound demonstrated the highest inhibitory activity against the hCA II isoform (Ki = 2.5 nM) with 30-, 9-, and 11-fold selectivity for hCA II over the I, IX, and XII isoforms, respectively. Structure-activity relationships for different substitution patterns were analyzed. Additionally, a molecular docking study showed that compound 3g bound to hCA II by coordinating with the zinc ion through the deprotonated benzenesulfonamide moiety, in addition to a hydrogen bond formed between an oxygen of the sulfonamide moiety and Thr199. Moreover, the chalcone core participated in van der Waals interactions with some active site residues, such as Ile91, Val121, and Leu198. Consequently, this report introduces a successful approach toward identifying compound 3g as a highly potent and selective chalcone-based benzenesulfonamide inhibitor of hCA II worthy of further investigation.

Marine-Derived Furanones Targeting Quorum-Sensing Receptors in Pseudomonas aeruginosa: Molecular Insights and Potential Mechanisms of Inhibition

The quorum-sensing (QS) machinery in disease-causing microorganisms is critical in developing antibiotic resistance. In Pseudomonas aeruginosa, QS is involved in biofilm formation, virulence factors production, and general tolerance to antimicrobials. Owing to the major role QS plays, interference in the process is probably a facile route to overcome antimicrobial resistance. Some furanone-derived compounds from marine sources have shown promising anti-QS activity. However, their protein targets and potential mechanisms of action have not been explored. To elucidate their potential protein targets in this study, marine metabolites with furanone backbones similar to their cognitive autoinducers (AIs) were screened against various QS receptors (LasR, RhlR, and PqsR) using molecular docking and molecular dynamics (MD) simulation techniques. The order by which the compounds bind to the receptors follows LasR > RhlR > PqsR. Compounds exhibited remarkable stability against LasR and RhlR, likely because the AIs of these receptors are structural analogs of furanones. Furanones with shorter alkyl side chains bound strongly against RhlR. The presence of halogens improved binding against various receptors. PqsR, with its hydrophobic-binding site and structurally different AIs, showed weaker binding. This study provides a molecular basis for the design of potent antagonists against QS receptors using marine-derived furanones.

Comprehensive insights into herbal P-glycoprotein inhibitors and nanoformulations for improving anti-retroviral therapy efficacy

The worldwide HIV cases were 39.0 million (33.1-45.7 million) in 2022. Due to genetic variations, HIV-1 is more easily transmitted than HIV-2 and favours CD4 + T cells and macrophages, producing AIDS. Conventional HIV drug therapy has many drawbacks, including adherence issues leading to resistance, side effects that lower life quality, drug interactions, high costs limiting global access, inability to eliminate viral reservoirs, chronicity requiring lifelong treatment, emerging toxicities, and a focus on managing infections. Conventional dosage forms have bioavailability issues due to intestinal P-glycoprotein (P-gp) efflux, which can reduce anti-retroviral drug efficacy and lead to resistance. Use of phyto-constituents with P-gp regulating actions has great benefits for semi-synthetic modification to create formulations with greater bioavailability and reduced toxicity, which improves drug effectiveness. Lipid-based nanocarriers, solid lipid nanoparticles, nanostructured lipid carriers, polymer-based nanocarriers, and inorganic nanoparticles may inhibit P-gp efflux. Employing potent P-gp inhibitors within nanocarriers as a Trojan horse approach can enhance the intracellular accumulation of anti-retroviral drugs (ARDs), which are substrates for efflux transporters. This technique increases oral bioavailability and offers lower-dose options, boosting HIV patient compliance and lowering costs. Molecular docking of the inhibitor with P-gp may anticipate optimum binding and function, allowing drug efflux to be minimised.

Natural autophagy activators: A promising strategy for combating photoaging

BACKGROUND

Photodamage to the skin stands out as one of the most widespread epidermal challenges globally. Prolonged exposure to sunlight containing ultraviolet radiation (UVR) instigates stress, thereby compromising the skin's functionality and culminating in photoaging. Recent investigations have shed light on the importance of autophagy in shielding the skin from photodamage. Despite the acknowledgment of numerous phytochemicals possessing photoprotective attributes, their potential to induce autophagy remains relatively unexplored.

PURPOSE

Diminished autophagy activity in photoaged skin underscores the potential benefits of restoring autophagy through natural compounds to enhance photoprotection. Consequently, this study aims to highlight the role of natural compounds in safeguarding against photodamage and to assess their potential to induce autophagy via an in-silico approach.

METHODS

A thorough search of the literature was done using several databases, including PUBMED, Science Direct, and Google Scholar, to gather relevant studies. Several keywords such as Phytochemical, Photoprotection, mTOR, Ultraviolet Radiation, Reactive oxygen species, Photoaging, and Autophagy were utilized to ensure thorough exploration. To assess the autophagy potential of phytochemicals through virtual screening, computational methodologies such as molecular docking were employed, utilizing tools like AutoDock Vina. Receptor preparation for docking was facilitated using MGLTools.

RESULTS

The initiation of structural and functional deterioration in the skin due to ultraviolet radiation (UVR) or sunlight-induced reactive oxygen species/reactive nitrogen species (ROS/RNS) involves the modulation of various pathways. Natural compounds like phenolics, flavonoids, flavones, and anthocyanins, among others, possess chromophores capable of absorbing light, thereby offering photoprotection by modulating these pathways. In our molecular docking study, these phytochemicals have shown binding affinity with mTOR, a negative regulator of autophagy, indicating their potential as autophagy modulators.

CONCLUSION

This integrated review underscores the photoprotective characteristics of natural compounds, while the in-silico analysis reveals their potential to modulate autophagy, which could significantly contribute to their anti-photoaging properties. The findings of this study hold promise for the advancement of cosmeceuticals and therapeutics containing natural compounds aimed at addressing photoaging and various skin-related diseases. By leveraging their dual benefits of photoprotection and autophagy modulation, these natural compounds offer a multifaceted approach to combatting skin aging and related conditions.

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

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

Proteasome and PARP1 dual-target inhibitor for multiple myeloma: Fluzoparib

One of the current mainstream treatments for multiple myeloma (MM) is chemotherapy. However, due to the high clonal heterogeneity and genomic complexity of MM, single-target drugs have limited efficacy and are prone to drug resistance. Therefore, there is an urgent need to develop multi-target drugs against MM. We screened drugs that simultaneously inhibit poly(ADP-ribose) polymerase 1 (PARP1) and 20S proteasome through computer-aided drug discovery (CADD) techniques, and explored the binding mode and dynamic stability of selected inhibitor to proteasome through Molecular biology (MD) simulation method. Thus, the dual-target inhibition effect of fluzoparib was proposed for the first time, and the ability of dual-target inhibition and tumor killing was explored at the enzyme, cell and animal level, respectively. This provides a theoretical and experimental basis for exploring multi-target inhibitory drugs for cancers.

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

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

Integrated transcriptomics- and structure-based drug repositioning identifies drugs with proteasome inhibitor properties

Computational pharmacogenomics can potentially identify new indications for already approved drugs and pinpoint compounds with similar mechanism-of-action. Here, we used an integrated drug repositioning approach based on transcriptomics data and structure-based virtual screening to identify compounds with gene signatures similar to three known proteasome inhibitors (PIs; bortezomib, MG-132, and MLN-2238). In vitro validation of candidate compounds was then performed to assess proteasomal proteolytic activity, accumulation of ubiquitinated proteins, cell viability, and drug-induced expression in A375 melanoma and MCF7 breast cancer cells. Using this approach, we identified six compounds with PI properties ((-)-kinetin-riboside, manumycin-A, puromycin dihydrochloride, resistomycin, tegaserod maleate, and thapsigargin). Although the docking scores pinpointed their ability to bind to the β5 subunit, our in vitro study revealed that these compounds inhibited the β1, β2, and β5 catalytic sites to some extent. As shown with bortezomib, only manumycin-A, puromycin dihydrochloride, and tegaserod maleate resulted in excessive accumulation of ubiquitinated proteins and elevated HMOX1 expression. Taken together, our integrated drug repositioning approach and subsequent in vitro validation studies identified six compounds demonstrating properties similar to proteasome inhibitors.

Exploring the potential of FDA approved anti-diabetic drugs for repurposing against COVID-19: a core combination of multiple computational strategies and integrated artificial intelligence

The latest variant of coronavirus is omicron. The World Health Organization (WHO) designated variation 'B.1.1.529' named omicron as a variant of concern (VOC) on 26 November 2021. By September 2020, it will have infected over 16 million patients and killed over 600,000 people over the world. This very infectious viral illness still poses a danger to world health; it has also become the greatest problem the world is facing and become the main area of research. The development of vaccines is insufficient to stop their spread and serious effects. Despite several reputable pharmaceutical firms claiming to have developed a cure for COVID-19. For that purpose, the field-based 3D-QSAR model has been used to analyze a series of anti-diabetic drugs to repurpose them against COVID-19. The LOO verified partial least square (PLS) model generates satisfactory q2 (0.4) and r2 (0.5) values. By using this model 10 compounds were screened out of 55 FDA approved anti-diabetic drugs (built-up library). Additionally, these substances were examined using molecular docking screening and ADMET. Finally, the drugs L8, and L23 were discovered to be the lead drugs. Density functional theory at the B3LYP/6-311G* technique was used to examine structural geometries, electronic characteristics, and molecular electrostatic potential (MEP). This work will greatly assist in the detection and development of leads for early drug development to control COVID-19.Communicated by Ramaswamy H. Sarma.

Identification of novel chemical scaffolds against kinase domain of cancer causing human epidermal growth factor receptor 2: a systemic chemoinformatic approach

The Human epidermal growth factor receptor 2 (HER2) is expressed in high magnitude in several cancers. Designing new drug molecules that target kinase domain of the HER2 enzyme might provide an appealing platform. Considering this, herein, a multi-phase bioinformatic approach is applied to screen diverse natural and chemical scaffolds to identify compounds that fit best at the kinase domain of HER2. By doing so, three compounds; LAS_51187157, LAC_51217113, LAC_51390233 were pointed with docking score of -11.4 kcal/mol, -11.3 kcal/mol and -11.2 kcal/mol, respectively. In molecular dynamic simulation, the complexes behaved in a stable dynamic with no major local/global structural variations. The intermolecular binding free energies were further estimated that concluded LAC_51390233 complex was the most stable and has less entropy energy. The good docked affinity of LAC_51390233 with HER2 was confirmed by WaterSwap absolute binding free energy. The entropy energy demonstrated that LAC_51390233 has less freedom energy compared to others. Similarly, all three compounds revealed very favorable druglike properties and pharmacokinetics. All the selected three compounds were also non-carcinogenic, non-immunotoxicity, non-mutagenicity, and non-cytotoxic. In a nutshell, the compounds are interesting scaffolds and might be subjected to extensive experimental testing to reveal their real biological potency.Communicated by Ramaswamy H. Sarma.

Machine learning and biological evaluation-based identification of a potential MMP-9 inhibitor, effective against ovarian cancer cells SKOV3

MMP-9, also known as gelatinase B, is a zinc-metalloproteinase family protein that plays a key role in the degradation of the extracellular matrix (ECM). The normal function of MMP-9 includes the breakdown of ECM, a process that aids in normal physiological processes such as embryonic development, angiogenesis, etc. Interruptions in these processes due to the over-expression or downregulation of MMP-9 are reported to cause some pathological conditions like neurodegenerative diseases and cancer. In the present study, an integrated approach for ML-based virtual screening of the Maybridge library was carried out and their biological activity was tested in an attempt to identify novel small molecule scaffolds that can inhibit the activity of MMP-9. The top hits were identified and selected for target-based activity against MMP-9 protein using the kit (Biovision K844). Further, MTT assay was performed in various cancer cell lines such as breast (MCF-7, MDA-MB-231), colorectal (HCT119, DL-D-1), cervical (HeLa), lung (A549) and ovarian cancer (SKOV3). Interestingly, one compound viz., RJF02215 exhibited anti-cancer activity selectively in SKOV3. Wound healing assay and colony formation assay performed on SKOV3 cell line in the presence of RJF02215 confirmed that the compound had a significant inhibitory effect on this cell line. Thus, we have identified a novel molecule that can inhibit MMP-9 activity in vitro and inhibits the proliferation of SKOV3 cells. Novel molecules based on the structure of RJF02215 may become a good value addition for the treatment of ovarian cancer by exhibiting selective MMP-9 activity.Communicated by Ramaswamy H. Sarma.

TPCA-1 compound, inhibiting testis-specific serine/threonine protein kinase 3 for potential male sterile in Bombyx mori

BACKGROUND

Protein kinases are a type of transferase enzyme that catalyze the phosphorylation of protein substrates, including receptor proteins. Testis-specific serine/threonine kinases (TSSKs) are a highly conserved group of protein kinases found in various organisms. They play an essential role in male reproduction by influencing sperm development and function.

RESULTS

In this study, we report on the characterization of BmTSSK3, a TSSK from the silkworm, Bombyx mori. We found that BmTSSK3 is specifically expressed in the testis and localized to the sperm flagella, particularly in the sperm tail cyst. Furthermore, we developed BmTSSK3 inhibitors through molecular docking and binding assays. Small molecules 5-(4-Fluorophenyl)-2-ureidothiophene-3-carboxamide (TPCA-1) and Imidurea were identified to bind to BmTSSK3. Using site-specific mutation technology, we identified amino acid residues R134 and S184 as crucial binding sites for small molecules. RNA interference assay and Western blot analysis showed that knockdown of BmTSSK3 significantly decreased histone 3 phosphorylation. To confirm the inhibitory effect of these small molecules, we treated silkworm testes with TPCA-1 and observed a strong inhibitory effect.

CONCLUSION

TPCA-1 is an inhibitor of BmTSSK3, which raises its potential as a future candidate for male sterility of the silkworm. Thus, this study may offer a novel strategy for sterile silkworms as well as insects. © 2024 Society of Chemical Industry.

Identification of novel covalent JAK3 inhibitors through consensus scoring virtual screening: integration of common feature pharmacophore and covalent docking

Accumulated research strongly indicates that Janus kinase 3 (JAK3) is intricately involved in the initiation and advancement of a diverse range of human diseases, underscoring JAK3 as a promising target for therapeutic intervention. However, JAK3 shows significant homology with other JAK family isoforms, posing substantial challenges in the development of JAK3 inhibitors. To address these limitations, one strategy is to design selective covalent JAK3 inhibitors. Therefore, this study introduces a virtual screening approach that combines common feature pharmacophore modeling, covalent docking, and consensus scoring to identify novel inhibitors for JAK3. First, common feature pharmacophore models were constructed based on a selection of representative covalent JAK3 inhibitors. The optimal qualitative pharmacophore model proved highly effective in distinguishing active and inactive compounds. Second, 14 crystal structures of the JAK3-covalent inhibitor complex were chosen for the covalent docking studies. Following validation of the screening performance, 5TTU was identified as the most suitable candidate for screening potential JAK3 inhibitors due to its higher predictive accuracy. Finally, a virtual screening protocol based on consensus scoring was conducted, integrating pharmacophore mapping and covalent docking. This approach resulted in the discovery of multiple compounds with notable potential as effective JAK3 inhibitors. We hope that the developed virtual screening strategy will provide valuable guidance in the discovery of novel covalent JAK3 inhibitors.

Pharmacophore-based virtual screening, molecular docking and molecular dynamics studies for the discovery of novel neuraminidase inhibitors

The in silico evaluation of 27 p-aminosalicylic acid derivatives, also referred to as neuraminidase inhibitors was the focus of the current study. To search and predict new potential neuraminidase inhibitors, this study was based on the ligand-based pharmacophore modeling, 3D QSAR, molecular docking, ADMET and MD simulation studies. The data was generated from recently reported inhibitors and divided into two groups, one of these group has 17 compounds for training and the second group has 10 compounds for testing purpose. The generated pharmacophore has known as ADDPR_4 was found statistically significant 3D-QSAR model owing the high trust scores (R2 = 0.974, Q2 = 0.905, RMSE = 0.23). Morever external validation was also employed to evaluate the prediction capacity of the built pharmacophore model (R2pred = 0.905). In addition, in silico ADMET, analyses were employed to evaluate the obtained hits for drug likeness properties. The stability of formed complexes was further evaluated using molecular dynamics. Top two hits showed stable complexes with Neuraminidase based on calculated total binding energy by MM-PBSA.Communicated by Ramaswamy H. Sarma.

Molecular Dynamic Simulations for Biopolymers with Biomedical Applications

Computational modeling (CM) is a versatile scientific methodology used to examine the properties and behavior of complex systems, such as polymeric materials for biomedical bioengineering. CM has emerged as a primary tool for predicting, setting up, and interpreting experimental results. Integrating in silico and in vitro experiments accelerates scientific advancements, yielding quicker results at a reduced cost. While CM is a mature discipline, its use in biomedical engineering for biopolymer materials has only recently gained prominence. In biopolymer biomedical engineering, CM focuses on three key research areas: (A) Computer-aided design (CAD/CAM) utilizes specialized software to design and model biopolymers for various biomedical applications. This technology allows researchers to create precise three-dimensional models of biopolymers, taking into account their chemical, structural, and functional properties. These models can be used to enhance the structure of biopolymers and improve their effectiveness in specific medical applications. (B) Finite element analysis, a computational technique used to analyze and solve problems in engineering and physics. This approach divides the physical domain into small finite elements with simple geometric shapes. This computational technique enables the study and understanding of the mechanical and structural behavior of biopolymers in biomedical environments. (C) Molecular dynamics (MD) simulations involve using advanced computational techniques to study the behavior of biopolymers at the molecular and atomic levels. These simulations are fundamental for better understanding biological processes at the molecular level. Studying the wide-ranging uses of MD simulations in biopolymers involves examining the structural, functional, and evolutionary aspects of biomolecular systems over time. MD simulations solve Newton's equations of motion for all-atom systems, producing spatial trajectories for each atom. This provides valuable insights into properties such as water absorption on biopolymer surfaces and interactions with solid surfaces, which are crucial for assessing biomaterials. This review provides a comprehensive overview of the various applications of MD simulations in biopolymers. Additionally, it highlights the flexibility, robustness, and synergistic relationship between in silico and experimental techniques.

ACE inhibitors from Suaeda salsa: 3D-QSAR modeling, metabolomics, molecular docking and molecular dynamics simulations

Inhibition of ACE is considered as one of the main strategies to reduce hypertension. ACE inhibitors derived from Suaeda salsa (S. salsa) present a novel antihypertensive agent source. This study employed 3D-QSAR pharmacophore, metabolomics, docking-based screening, and molecular dynamics simulations to identify ACE inhibitors from S. salsa. A set of 53 known molecules was chemically diverse to construct a 3D-QSAR model for predictive purposes. S. salsa was characterized using UPLC-QqQ-MS/MS and UPLC-Q-TOF-LC-MS techniques, 211 and 586 kinds of bioactive metabolites were identified, respectively. A total of 680 compounds were collected for database construction and virtual screening. An ADMET assessment was conducted to evaluate drug-likeness and pharmacokinetics parameters. Moreover, molecular docking results show that six top hit compounds bind to ACE tightly. Specially, diosmin could interact with ACE by hydrogen bond, Pi-cation bond, and metal bond. Molecular dynamics (MD) simulation and MMPBSA calculations were subsequently employed to elucidate complex stability and the interaction between diosmin and ACE, indicating it a strong ACE inhibitory activity. In conclusion, this study suggests that S.salsa represents a potential source of antihypertensive agents.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-024-00233-0.

Anti-Melanogenic Activity of Ethanolic Extract from Garcinia atroviridis Fruits Using In Vitro Experiments, Network Pharmacology, Molecular Docking, and Molecular Dynamics Simulation

Melanin, the pigment responsible for human skin color, increases susceptibility to UV radiation, leading to excessive melanin production and hyperpigmentation disorders. This study investigated the ethanolic extract of Garcinia atroviridis fruits for its phenolic and flavonoid contents, antioxidant activity, and impact on melanogenesis pathways using qRT-PCR and Western blot analysis. Utilizing network pharmacology, molecular docking, and dynamics simulations, researchers explored G. atroviridis fruit extract's active compounds, targets, and pharmacological effects on hyperpigmentation. G. atroviridis fruit extract exhibited antioxidant properties, scavenging DPPH• and ABTS•+ radicals radicals and chelating copper. It inhibited cellular tyrosinase activity and melanin content in stimulated B16F10 cells, downregulating TYR, TRP-1, phosphorylated CREB, CREB, and MITF proteins along with transcription levels of MITF, TYR, and TRP-2. LC-MS analysis identified thirty-three metabolites, with seventeen compounds selected for further investigation. Network pharmacology revealed 41 hyperpigmentation-associated genes and identified significant GO terms and KEGG pathways, including cancer-related pathways. Kaempferol-3-O-α-L-rhamnoside exhibited high binding affinity against MAPK3/ERK1, potentially regulating melanogenesis by inhibiting tyrosinase activity. Stable ligand-protein interactions in molecular dynamics simulations supported these findings. Overall, this study suggests that the ethanolic extract of G. atroviridis fruits possesses significant antioxidant, tyrosinase inhibitory, and anti-melanogenic properties mediated through key molecular targets and pathways.

Screening approach by a combination of computational and in vitro experiments: identification of fluvastatin sodium as a potential SIRT2 inhibitor

BACKGROUND

Sirtuins (SIRTs) are NAD+-dependent deacetylases that play various roles in numerous pathophysiological processes, holding promise as therapeutic targets worthy of further investigation. Among them, the SIRT2 subtype is closely associated with tumorigenesis and malignancies. Dysregulation of SIRT2 activation can regulate the expression levels of related genes in cancer cells, leading to tumor occurrence and metastasis.

METHODS

In this study, we used computer simulations to screen for novel SIRT2 inhibitors from the FDA database, based on which 10 compounds with high docking scores and good interactions were selected for in vitro anti-pancreatic cancer metastasis testing and enzyme binding inhibition experiments. The results showed that fluvastatin sodium may possess inhibitory activity against SIRT2. Subsequently, fluvastatin sodium was subjected to molecular docking experiments with various SIRT isoforms, and the combined results from Western blotting experiments indicated its potential as a SIRT2 inhibitor. Next, molecular docking, molecular dynamics (MD) simulations, and binding free energy calculations were performed, revealing the binding mode of fluvastatin sodium at the SIRT2 active site, further validating the stability and interaction of the ligand-protein complex under physiological conditions.

RESULTS

Overall, this study provides a systematic virtual screening workflow for the discovery of SIRT2 activity inhibitors, identifies the potential inhibitory effect of fluvastatin sodium as a lead compound on SIRT2, and opens up a new direction for developing highly active and selectively targeted SIRT2 inhibitors.

Molecular docking based comparative study of antiviral compounds on SARS-CoV-2 spike protein

The urgent need for effective therapeutic interventions against SARS-CoV-2 has prompted extensive exploration of potential drug candidates. Among the viral proteins, the spike (S) protein presents an attractive target due to its critical role in viral entry and infection. In this study, we employed molecular docking techniques to investigate the binding affinities and interaction profiles of a panel of active compounds against the SARS-CoV-2 spike protein. Utilising computational simulations, we assessed the binding properties of these compounds within the receptor-binding domain (RBD) and other key regions of the spike protein. Our comparative analysis elucidates the differential binding patterns and identifies promising lead compounds with high binding affinity and favourable interaction profiles. Furthermore, we discuss the implications of these findings for the development of potential therapeutics targeting the SARS-CoV-2 spike protein. Using molecular docking and the Lipinski five rule, this study illustrates possible compounds with strong binding affinities, their molecular interactions, for both naturally occurring and man-made drugs. Computational approach is applied, and it is concluded that, drugs like Withanolide, Dihydroergocristine, Fenebrutinib, and Ergotamine shows binding energies between -8.3 and -9.1 kcal/mol, and are possible candidate for anti covid drug.

An interdisciplinary course on computer-aided drug discovery to broaden student participation in original scientific research

We present a new highly interdisciplinary project-based course in computer aided drug discovery (CADD). This course was developed in response to a call for alternative pedagogical approaches during the COVID-19 pandemic, which caused the cancellation of a face-to-face summer research program sponsored by the Louisiana Biomedical Research Network (LBRN). The course integrates guided research and educational experiences for chemistry, biology, and computer science students. We implement research-based methods with publicly available tools in bioinformatics and molecular modeling to identify and prioritize promising antiviral drug candidates for COVID-19. The purpose of this course is three-fold: I. Implement an active learning and inclusive pedagogy that fosters student engagement and research mindset; II. Develop student interdisciplinary research skills that are highly beneficial in a broader scientific context; III. Demonstrate that pedagogical shifts (initially incurred during the COVID-19 pandemic) can furnish longer-term instructional benefits. The course, which has now been successfully taught a total of five times, incorporates four modules, including lectures/discussions, live demos, inquiry-based assignments, and science communication.

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

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

Antimicrobial Activity Classification of Imidazolium Derivatives Predicted by Artificial Neural Networks

PURPOSE

This study assesses the Multilayer Perceptron (MLP) neural network, complemented by other Machine Learning techniques (CART, PCA), in predicting the antimicrobial activity of 140 newly designed imidazolium chlorides against Klebsiella pneumoniae before synthesis. Emphasis is on leveraging molecular properties for predictive analysis.

METHODS

Classification and regression decision trees (CART) identified the top 200 predictive molecular descriptors. Principal Component Analysis (PCA) reduced these descriptors to 5 components, retaining 99.57% of raw data information. Antimicrobial activity, categorized as high or low, was based on experimentally proven minimal inhibitory concentration (MIC), with a cut-point at MIC = 0.856 mol/L. A 12-fold cross-validation trained the MLP (architecture 5-12-2 with 5 Principal Components).

RESULTS

The MLP exhibited commendable performance, achieving almost 90% correct classifications across learning, validation, and test sets, outperforming models without PCA dimension reduction. Key metrics, including accuracy (0.907), sensitivity (0.905), specificity (0.909), and precision (0.891), were notably high. These results highlight the MLP model's efficacy with PCA as a high-quality classifier for determining antimicrobial activity.

CONCLUSIONS

The study concludes that the MLP neural network, along with CART and PCA, is a robust tool for predicting the antimicrobial activity class of imidazolium chlorides against Klebsiella pneumoniae. CART and PCA, used in this study, allowed input variable reduction without significant information loss. High classification accuracy and associated metrics affirm the method's potential utility in pre-synthesis assessments, offering valuable insights for antimicrobial compound design.

Based on Virtual Screening and Simulation Exploring the Mechanism of Plant-Derived Compounds with PINK1 to Postherpetic Neuralgia

Accumulating evidence strongly supports that PINK1 mutation can mediate mitochondrial autophagy dysfunction in dopaminergic neurons. This study was conducted to determine the role of PINK1 in the pathogenesis of postherpetic neuralgia (PHN) and find new targets for its treatment. A rigorous literature review was conducted to identify 2801 compounds from more than 200 plants in Asia. Virtual screening was used to shortlist the compounds into 20 groups based on their binding energies. MM/PBSA was used to further screen the compound dataset, and vitexin, luteoloside, and 2'-deoxyadenosine-5'-monophosphate were found to have a score of - 59.439, - 52.421, and - 47.544 kcal/mol, respectively. Pain behavioral quantification, enzyme-linked immunosorbent assay, quantitative polymerase chain reaction, western blotting, and transmission electron microscopy were used to confirm the effective mechanism. Vitexin had the most significant therapeutic effect on rats with PHN followed by luteoloside; 2'-deoxyadenosine-5'-monophosphate had no significant effect. Our findings suggested that vitexin could alleviate PHN by regulating mitochondrial autophagy through PINK1.

Discovery of Novel Pyrazole Acyl Thiourea Skeleton Analogue as Potential Herbicide Candidates

To discover novel transketolase (TKL, EC 2.2.1.1) inhibitors with potential herbicidal applications, a series of pyrazole acyl thiourea derivatives were designed based on a previously obtained pyrazolamide acyl lead compound, employing a scaffold hopping strategy. The compounds were synthesized, their structures were characterized, and they were evaluated for herbicidal activities. The results indicate that 7a exhibited exceptional herbicidal activity against Digitaria sanguinalis and Amaranthus retroflexus at a dosage of 90 g ai/ha, using the foliar spray method in a greenhouse. This performance is comparable to that of commercial products, such as nicosulfuron and mesotrione. Moreover, 7a showed moderate growth inhibitory activity against the young root and stem of A. retroflexus at 200 mg/L in the small cup method, similar to that of nicosulfuron and mesotrione. Subsequent mode-of-action verification experiments revealed that 7a and 7e inhibited Setaria viridis TKL (SvTKL) enzyme activity, with IC50 values of 0.740 and 0.474 mg/L, respectively. Furthermore, they exhibited inhibitory effects on the Brassica napus acetohydroxyacid synthase enzyme activity. Molecular docking predicted potential interactions between these (7a and 7e) and SvTKL. A greenhouse experiment demonstrated that 7a exhibited favorable crop safety at 150 g ai/ha. Therefore, 7a is a promising herbicidal candidate that is worthy of further development.

DiffDec: Structure-Aware Scaffold Decoration with an End-to-End Diffusion Model

In molecular optimization, one popular way is R-group decoration on molecular scaffolds, and many efforts have been made to generate R-groups based on deep generative models. However, these methods mostly use information on known binding ligands, without fully utilizing target structure information. In this study, we proposed a new method, DiffDec, to involve 3D pocket constraints by a modified diffusion technique for optimizing molecules through molecular scaffold decoration. For end-to-end generation of R-groups with different sizes, we designed a novel fake atom mechanism. DiffDec was shown to be able to generate structure-aware R-groups with realistic geometric substructures by the analysis of bond angles and dihedral angles and simultaneously generate multiple R-groups for one scaffold on different growth anchors. The growth anchors could be provided by users or automatically determined by our model. DiffDec achieved R-group recovery rates of 69.67% and 45.34% in the single and multiple R-group decoration tasks, respectively, and these values were significantly higher than competing methods (37.33% and 26.85%). According to the molecular docking study, our decorated molecules obtained a better average binding affinity than baseline methods. The docking pose analysis revealed that DiffDec could decorate scaffolds with R-groups that exhibited improved binding affinities and more favorable interactions with the pocket. These results demonstrated the potential and applicability of DiffDec in real-world scaffold decoration for molecular optimization.

Identification of potential FAK inhibitors using mol2vec molecular descriptor-based QSAR, molecular docking, ADMET study, and molecular dynamics simulation

This study aims to identify potential focal adhesion kinase (FAK) inhibitors through an integrated computational approach, combining mol2vec descriptor-based QSAR, molecular docking, ADMET study, and molecular dynamics simulation. A dataset of 437 compounds with known FAK inhibitory activities was used to develop QSAR models using machine learning algorithms combined with mol2vec descriptors. Subsequently, the most promising compounds were subjected to molecular docking against FAK to evaluate their binding affinities and key interactions. ADMET study and molecular dynamics simulation were also employed to investigate the pharmacokinetic, drug-like properties, and the stability of the protein-ligand complexes. The results showed that the mol2vec descriptor-based QSAR model established by support vector regression demonstrated good predictive performance (R2 = 0.813, RMSE = 0.453, MAE = 0.263 in case of training set, and R2 = 0.729, RMSE = 0.635, MAE = 0.477 in case of test set), indicating their reliability in identifying potent FAK inhibitors. Using this QSAR model and molecular docking, compound 21 (ZINC000004523722) was identified as the most potential compound, with predicted logIC50 value and binding energy of 2.59 and - 9.3 kcal/mol, respectively. The results of molecular dynamics simulation and ADMET study also further suggested its potential as a promising drug candidate. However, because our research was merely theoretical, additional in vitro and in vivo studies are required for the verification of these results.

Predictive Modelling in pharmacokinetics: from in-silico simulations to personalized medicine

INTRODUCTION

Pharmacokinetic parameters assessment is a critical aspect of drug discovery and development, yet challenges persist due to limited training data. Despite advancements in machine learning and in-silico predictions, scarcity of data hampers accurate prediction of drug candidates' pharmacokinetic properties.

AREAS COVERED

The study highlights current developments in human pharmacokinetic prediction, talks about attempts to apply synthetic approaches for molecular design, and searches several databases, including Scopus, PubMed, Web of Science, and Google Scholar. The article stresses importance of rigorous analysis of machine learning model performance in assessing progress and explores molecular modeling (MM) techniques, descriptors, and mathematical approaches. Transitioning to clinical drug development, article highlights AI (Artificial Intelligence) based computer models optimizing trial design, patient selection, dosing strategies, and biomarker identification. In-silico models, including molecular interactomes and virtual patients, predict drug performance across diverse profiles, underlining the need to align model results with clinical studies for reliability. Specialized training for human specialists in navigating predictive models is deemed critical. Pharmacogenomics, integral to personalized medicine, utilizes predictive modeling to anticipate patient responses, contributing to more efficient healthcare system. Challenges in realizing potential of predictive modeling, including ethical considerations and data privacy concerns, are acknowledged.

EXPERT OPINION

AI models are crucial in drug development, optimizing trials, patient selection, dosing, and biomarker identification and hold promise for streamlining clinical investigations.

Structure-based virtual screening of anti-breast cancer compounds from Artemisia absinthium-insights through molecular docking, pharmacokinetics, and molecular dynamic simulations

Breast cancer is the world's second most frequent malignancy, with a significant mortality and morbidity rate. Nowadays, natural breast cancer medicine has piqued attention as disease-curing agent with low side effects. Herein, the leaf powder of Artemisia absinthium was extracted with ethanol, and GC-MS and LC-MS methods were employed to identify the phytocompounds. Using commercial software SeeSAR-9.2 and StarDrop, identified phytocompounds were docked with estrogen and progesterone breast cancer receptors as they promote breast cancer growth to find the binding affinity of the ligands, drugability, and toxicity. Hormone-mediated breast cancer accounts for about 80% of all cases of breast cancer. Cancer cells proliferate when estrogen and progesterone hormones are attached to these receptors. The molecular docking results demonstrated that 3',4',5,7-Tetrahydroxyisoflavanone (THIF) has stronger binding efficacy than standard drugs and other phytocompounds with -28.71 (3 hydrogen bonds) and -24.18 kcal/mol (6 hydrogen bonds) binding energies for estrogen and progesterone receptors, respectively. Pharmacokinetics and toxicity analysis were done to predict the drug-likeness of THIF which results in good drugability and less toxicity. The best fit THIF was subjected to a molecular dynamics simulation analysis by using Gromacs to analyze the conformational changes that occurred during protein-ligand interaction and found that, the structural changes were observed. The results from MD simulation and pharmacokinetic studies suggested that THIF can be expected that in vitro and in vivo research on this compound may lead to the development of a potent anti-breast cancer drug in the future.Communicated by Ramaswamy H. Sarma.

Targeting XGHPRT enzyme to manage Helicobacter pylori induced gastric cancer: A multi-pronged machine learning, artificial intelligence and biophysics-based study

Helicobacter pylori infects the stomach mucosa of over half of the global population and can lead to gastric cancer. This pathogen has demonstrated resistance to many frequently prescribed antibiotics, thereby underscoring the pressing need to identify novel therapeutic targets. The inhibition or disruption of nucleic acid biosynthesis constitutes a promising avenue for either restraining or eradicating bacterial proliferation. The synthesis of RNA and DNA precursors (6-oxopurine nucleoside monophosphates) is catalyzed by the XGHPRT enzyme. In this study, using machine learning, artificial intelligence and biophysics-based software, CHEMBRIDGE-10000196, CHEMBRIDGE-10000295, and CHEMBRIDGE-10000955 were predicted as promising binders to the XGHPRT with a binding score of -14.20, -13.64, and -12.08 kcal/mol, respectively, compared to a control guanosine-5'-monophosphate exhibiting a docking score of -10.52 kcal/mol. These agents formed strong interactions with Met33, Arg34, Ala57, Asp92, Ser93, and Gly94 at short distance. The docked complexes of the lead compounds exhibited stable dynamics during the simulation time with no global changes noticed. The docked complexes demonstrate a significantly stable MM-GBSA and MM-PBSA net binding energy of -60.1 and -61.18 kcal/mol for the CHEMBRIDGE-10000196 complex. The MM-GBSA net energy value of the CHEMBRIDGE-10000295 complex and the CHEMBRIDGE-10000955 complex is -71.17 and -65.29 kcal/mol, respectively. The CHEMBRIDGE-10000295 and CHEMBRIDGE-10000955 complexes displayed a net value of -71.91 and -63.49 kcal/mol, respectively, as per the MM-PBSA. The major driving intermolecular interactions for the docked complexes were found to be the electrostatic and van der Waals. The three filtered molecules hold potential for experimental evaluation of their potency against the XGHPRT enzyme.

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.

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.

Identification of potent BRD4-BD1 inhibitors using classical and steered molecular dynamics based free energy analysis

In the present work a combination of traditional and steered molecular dynamics based techniques were employed to identify potential inhibitors against the human BRD4 protein (BRD4- BD1); an established drug target for multiple illnesses including various malignancies. Quinoline derivatives that were synthesized in-house were tested for their potential as new BRD4-BD1 inhibitors. Initially molecular docking experiments were performed to determine the binding poses of BRD4-BD1 inhibitors. To learn more about the thermodynamics of inhibitor binding to the BRD4-BD1 active site, the Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) free energy calculations were conducted afterwards. The findings of the MM-PBSA analysis were further reinforced by performing steered umbrella sampling simulations which revealed crucial details about the binding/unbinding process of the most potent quinoline derivatives at the BRD4-BD1 active site. We report a novel quinoline derivative which can be developed into a fully functional BRD4-BD1 inhibitor after experimental validation. The identified compound (4 g) shows better properties than the standard BRD4-BD1 inhibitors considered in the study. The study also highlights the crucial role of Gln78, Phe79, Trp81, Pro82, Phe83, Gln84, Gln85, Val87, Leu92, Leu94, Tyr97, Met105, Cys136, Asn140, Ile146 and Met149 in inhibitor binding. The study provides a possible lead candidate and key amino acids involved in inhibitor recognition and binding at the active site of BRD4-BD1 protein. The findings might be of significance to medicinal chemists involved in the development of potent BRD4-BD1 inhibitors.

Assessment of the Anti-Breast Cancer Effects of Urolithin with Molecular Docking Studies in the In Vitro Condition: Introducing a Novel Chemotherapeutic Drug

A lot of research has been done on using natural items as diabetes treatment. The molecular docking study was conducted to evaluate the inhibitory activities of urolithin A against α-amylase, α-glucosidase, and aldose reductase. The molecular docking calculations indicated the probable interactions and the characteristics of these contacts at an atomic level. The results of the docking calculations showed the docking score of urolithin A against α-amylase was -5.169 kcal/mol. This value for α-glucosidase and aldose reductase was -3.657 kcal/mol and -7.635 kcal/mol, respectively. In general, the outcomes of the docking calculations revealed that urolithin A can construct several hydrogen bonds and hydrophobic contacts with the assessed enzymes and reduces their activities considerably. The properties of urolithin against common human breast cancer cell lines, i.e., SkBr3, MDA-MB-231, MCF-7, Hs578T, Evsa-T, BT-549, AU565 and 600MPE were evaluated. The IC50 of the urolithin was 400, 443, 392, 418, 397, 530, 566 and 551 against SkBr3, MDA-MB-231, MCF-7, Hs578T, Evsa-T, BT-549, AU565 and 600MPE, respectively. After doing the clinical trial studies, the recent molecule may be used as an anti-breast cancer supplement in humans. IC50 values of urolithin A on α-amylase, α-glucosidase, and aldose reductase enzymes were obtained at 16.14, 1.06 and 98.73 µM, respectively.

Integrating multiscale and machine learning approaches towards the SAMPL9 log P challenge

The partition coefficient (log P) is an important physicochemical property that provides information regarding a molecule's pharmacokinetics, toxicity, and bioavailability. Methods to accurately predict the partition coefficient have the potential to accelerate drug design. In an effort to test current methods and explore new computational techniques, the statistical assessment of the modeling of proteins and ligands (SAMPL) has established a blind prediction challenge. The ninth iteration challenge was to predict the toluene-water partition coefficient (log Ptol/w) of sixteen drug molecules. Herein, three approaches are reported broadly under the categories of quantum mechanics (QM), molecular mechanics (MM), and data-driven machine learning (ML). The three blind submissions yield mean unsigned errors (MUE) ranging from 1.53-2.93 log Ptol/w units. The MUEs were reduced to 1.00 log Ptol/w for the QM methods. While MM and ML methods outperformed DFT approaches for challenge molecules with fewer rotational degrees of freedom, they suffered for the larger molecules in this dataset. Overall, DFT functionals paired with a triple-ζ basis set were the simplest and most effective tool to obtain quantitatively accurate partition coefficients.

Identification of potential inhibitor against Leishmania donovani mitochondrial DNA primase through in-silico and in vitro drug repurposing approaches

Leishmania donovani is the causal organism of leishmaniasis with critical health implications affecting about 12 million people around the globe. Due to less efficacy, adverse side effects, and resistance, the available therapeutic molecules fail to control leishmaniasis. The mitochondrial primase of Leishmania donovani (LdmtPRI1) is a vital cog in the DNA replication mechanism, as the enzyme initiates the replication of the mitochondrial genome of Leishmania donovani. Hence, we target this protein as a probable drug target against leishmaniasis. The de-novo approach enabled computational prediction of the three-dimensional structure of LdmtPRI1, and its active sites were identified. Ligands from commercially available drug compounds were selected and docked against LdmtPRI1. The compounds were chosen for pharmacokinetic study and molecular dynamics simulation based on their binding energies and protein interactions. The LdmtPRI1 gene was cloned, overexpressed, and purified, and a primase activity assay was performed. The selected compounds were verified experimentally by the parasite and primase inhibition assay. Capecitabine was observed to be effective against the promastigote form of Leishmania donovani, as well as inhibiting primase activity. This study's findings suggest capecitabine might be a potential anti-leishmanial drug candidate after adequate further studies.

The role and potential of computer-aided drug discovery strategies in the discovery of novel antimicrobials

Antimicrobial resistance (AMR) has become more of a concern in recent decades, particularly in infections associated with global public health threats. The development of new antibiotics is crucial to ensuring infection control and eradicating AMR. Although drug discovery and development are essential processes in the transformation of a drug candidate from the laboratory to the bedside, they are often very complicated, expensive, and time-consuming. The pharmaceutical sector is continuously innovating strategies to reduce research costs and accelerate the development of new drug candidates. Computer-aided drug discovery (CADD) has emerged as a powerful and promising technology that renews the hope of researchers for the faster identification, design, and development of cheaper, less resource-intensive, and more efficient drug candidates. In this review, we discuss an overview of AMR, the potential, and limitations of CADD in AMR drug discovery, and case studies of the successful application of this technique in the rapid identification of various drug candidates. This review will aid in achieving a better understanding of available CADD techniques in the discovery of novel drug candidates against resistant pathogens and other infectious agents.

Integrating computational methods guided the discovery of phytochemicals as potential Pin1 inhibitors for cancer: pharmacophore modeling, molecular docking, MM-GBSA calculations and molecular dynamics studies

Pin1 is a pivotal player in interactions with a diverse array of phosphorylated proteins closely linked to critical processes such as carcinogenesis and tumor suppression. Its axial role in cancer initiation and progression, coupled with its overexpression and activation in various cancers render it a potential candidate for the development of targeted therapeutics. While several known Pin1 inhibitors possess favorable enzymatic profiles, their cellular efficacy often falls short. Consequently, the pursuit of novel Pin1 inhibitors has gained considerable attention in the field of medicinal chemistry. In this study, we employed the Phase tool from Schrödinger to construct a structure-based pharmacophore model. Subsequently, 449,008 natural products (NPs) from the SN3 database underwent screening to identify compounds sharing pharmacophoric features with the native ligand. This resulted in 650 compounds, which then underwent molecular docking and binding free energy calculations. Among them, SN0021307, SN0449787 and SN0079231 showed better docking scores with values of -9.891, -7.579 and -7.097 kcal/mol, respectively than the reference compound (-6.064 kcal/mol). Also, SN0021307, SN0449787 and SN0079231 exhibited lower free binding energies (-57.12, -49.81 and -46.05 kcal/mol, respectively) than the reference ligand (-37.75 kcal/mol). Based on these studies, SN0021307, SN0449787, and SN0079231 showed better binding affinity that the reference compound. Further the validation of these findings, molecular dynamics simulations confirmed the stability of the ligand-receptor complex for 100 ns with RMSD ranging from 0.6 to 1.8 Å. Based on these promising results, these three phytochemicals emerge as promising lead compounds warranting comprehensive biological screening in future investigations. These compounds hold great potential for further exploration regarding their efficacy and safety as Pin1 inhibitors, which could usher in new avenues for combating cancer.

Exploring Citrus sinensis Phytochemicals as Potential Inhibitors for Breast Cancer Genes BRCA1 and BRCA2 Using Pharmacophore Modeling, Molecular Docking, MD Simulations, and DFT Analysis

BACKGROUND

Structure-activity relationship (SAR) is considered to be an effective in silico approach when discovering potential antagonists for breast cancer due to gene mutation. Major challenges are faced by conventional SAR in predicting novel antagonists due to the discovery of diverse antagonistic compounds. Methodologyand Results: In predicting breast cancer antagonists, a multistep screening of phytochemicals isolated from the seeds of the Citrus sinensis plant was applied using feasible complementary methodologies. A three-dimensional quantitative structure-activity relationship (3D-QSAR) model was developed through the Flare project, in which conformational analysis, pharmacophore generation, and compound alignment were done. Ten hit compounds were obtained through the development of the 3D-QSAR model. For exploring the mechanism of action of active compounds against cocrystal inhibitors, molecular docking analysis was done through Molegro software (MVD) to identify lead compounds. Three new proteins, namely, 1T15, 3EU7, and 1T29, displayed the best Moldock scores. The quality of the docking study was assessed by a molecular dynamics simulation. Based on binding affinities to the receptor in the docking studies, three lead compounds (stigmasterol P8, epoxybergamottin P28, and nobiletin P29) were obtained, and they passed through absorption, distribution, metabolism, and excretion (ADME) studies via the SwissADME online service, which proved that P28 and P29 were the most active allosteric inhibitors with the lowest toxicity level against breast cancer. Then, density functional theory (DFT) studies were performed to measure the active compound's reactivity, hardness, and softness with the help of Gaussian 09 software.

CONCLUSIONS

This multistep screening of phytochemicals revealed high-reliability antagonists of breast cancer by 3D-QSAR using flare, docking analysis, and DFT studies. The present study helps in providing a proper guideline for the development of novel inhibitors of BRCA1 and BRCA2.

Identification and validation of platelet-related diagnostic markers and potential drug screening in ischemic stroke by integrating comprehensive bioinformatics analysis and machine learning

Background

Ischemic stroke (IS), caused by blood and oxygen deprivation due to cerebral thrombosis, has links to activated and aggregated platelets. Discovering platelet-related biomarkers, developing diagnostic models, and screening antiplatelet drugs are crucial for IS diagnosis and treatment.

Methods and results

Combining and normalizing GSE16561 and GSE22255 datasets identified 1,753 upregulated and 1,187 downregulated genes. Fifty-one genes in the platelet-related module were isolated using weighted gene co-expression network analysis (WGCNA) and other analyses, including 50 upregulated and one downregulated gene. Subsequent enrichment and network analyses resulted in 25 platelet-associated genes and six diagnostic markers for a risk assessment model. This model's area under the ROC curve outperformed single genes, and in the peripheral blood of the high-risk group, immune infiltration indicated a higher proportion of CD4, resting CD4 memory, and activated CD4 memory T cells, along with a lower proportion of CD8 T cells in comparison to the low-risk group. Utilizing the gene expression matrix and the CMap database, we identified two potential drugs for IS. Finally, a rat MACO/R model was used to validate the diagnostic markers' expression and the drugs' predicted anticoagulant effects.

Conclusion

We identified six IS platelet-related biomarkers (APP, THBS1, F13A1, SRC, PPBP, and VCL) for a robust diagnostic model. The drugs alpha-linolenic acid and ciprofibrate have potential antiplatelet effects in IS. This study advances early IS diagnosis and treatment.

Discovery of potent DNMT1 inhibitors against sickle cell disease using structural-based virtual screening, MM-GBSA and molecular dynamics simulation-based approaches

Sickle cell disease (SCD) is an autosomal recessive genetic disorder affecting millions of people worldwide. A reversible and selective DNMT1 inhibitor, GSK3482364, has been known to decrease the overall methylation activity of DNMT1, resulting in the increase of HbF levels and percentage of HbF-expressing erythrocytes in an in vitro and in vivo model. In this study, a structure-based virtual screening was done with GSK3685032, a co-crystalized ligand of DNMT1 (PDB ID: 6X9K) with an IC50 value of 0.036 μM and identified 3988 compounds from three databases (ChEMBL, PubChem and Drug Bank). Using this screening method, we identified around 15 compounds with XP docking scores greater than -8 kcal/mol. Further, prime MM-GBSA calculations have been performed and found compound SCHEMBL19716714 with the highest binding free energy of -83.31 kcal/mol. Finally, four compounds were identified based on glide energy and ΔG bind scores that have the most binding with DG7, DG19, DG20 bases and Lys1535, His1507, Trp1510, Ser1230, which were required for the target enzyme inhibition. Furthermore, molecular dynamics simulation studies of top ligands validate the stability of the docked complexes by examining root mean square deviations, root mean square fluctuations, solvent accessible surface area, and radius of gyration graphs from simulation trajectories. These findings suggest that the top four hit compounds may be capable of inhibiting DNMT1 and that additional in vitro and in vivo studies will be essential to prove the clinical effectiveness of the selected lead compounds.Communicated by Ramaswamy H. Sarma.

Applications of Big Data and AI-Driven Technologies in CADD (Computer-Aided Drug Design)

In the field of computer-aided drug design (CADD), there has been dramatic progress in the development of big data and AI-driven methodologies. The expensive and time-consuming process of drug design is related to biomedical complexity. CADD can be used to apply effective and efficient strategies to overcome obstacles in the field of drug design in order to properly design and develop a new medicine. To prepare the raw data for consistent and repeatable applications of big data and AI methodologies, data pre-processing methods are introduced. Big data and AI technologies can be used to develop drugs in areas including predicting absorption, distribution, metabolism, excretion, and toxicity properties as well as finding binding sites in target proteins and conducting structure-based virtual screenings. The accurate and thorough analysis of large amounts of biomedical data as well as the design of prediction models in the area of drug design is made possible by data pre-processing and applications of big data and AI skills. In the biomedical big data era, knowledge on the biological, chemical, or pharmacological structures of biomedical entities relevant to drug design should be analyzed with significant big data and AI approaches.

Combined Structure- and Ligand-Based Approach for the Identification of Inhibitors of AcrAB-TolC in Escherichia coli

The inhibition of efflux pumps is a promising approach to combating multidrug-resistant bacteria. We have developed a combined structure- and ligand-based model, using OpenEye software, for the identification of inhibitors of AcrB, the inner membrane protein component of the AcrAB-TolC efflux pump in Escherichia coli. From a database of 1391 FDA-approved drugs, 23 compounds were selected to test for efflux inhibition in E. coli. Seven compounds, including ivacaftor (25), butenafine (19), naftifine (27), pimozide (30), thioridazine (35), trifluoperazine (37), and meloxicam (26), enhanced the activity of at least one antimicrobial substrate and inhibited the efflux pump-mediated removal of the substrate Nile Red from cells. Ivacaftor (25) inhibited efflux dose dependently, had no effect on an E. coli strain with genomic deletion of the gene encoding AcrB, and did not damage the bacterial outer membrane. In the presence of a sub-minimum inhibitory concentration (MIC) of the outer membrane permeabilizer colistin, ivacaftor at 1 μg/mL reduced the MICs of erythromycin and minocycline by 4- to 8-fold. The identification of seven potential AcrB inhibitors shows the merits of a combined structure- and ligand-based approach to virtual screening.

Integrated virtual screening, molecular modeling and machine learning approaches revealed potential natural inhibitors for epilepsy

Epilepsy, a prevalent chronic disorder of the central nervous system, is typified by recurrent seizures. Present treatments predominantly offer symptomatic relief by managing seizures, yet fall short of influencing epileptogenesis. This study endeavored to identify novel phytochemicals with potential therapeutic efficacy against S100B, an influential protein in epileptogenesis, through an innovative application of machine learning-enabled virtual screening. Our study incorporated the use of multiple machine learning algorithms, including Support Vector Machine (SVM), k-Nearest Neighbors (kNN), Naive Bayes (NB), and Random Forest (RF). These algorithms were employed not only for virtual screening but also for essential feature extraction and selection, enhancing our ability to distinguish between active and inactive compounds. Among the tested machine learning algorithms, the RF model outshone the rest, delivering an impressive 93.43 % accuracy on both training and test datasets. This robust RF model was leveraged to sift through the library of 9,000 phytochemicals, culminating in the identification of 180 potential inhibitors of S100B. These 180 active compounds were than docked with the active site of S100B proteins. The results of our study highlighted that the 6-(3,12-dihydroxy-4,10,13-trimethyl-7,11-dioxo-2,3,4,5,6,12,14,15,16,17-decahydro-1H cyclopenta[a] phenanthren -17-yl)-2-methyl-3-methylideneheptanoic acid, rhinacanthin K, thiobinupharidine, scopadulcic acid, and maslinic acid form significant interactions within the binding pocket of S100B, resulting in stable complexes. This underscores their potential role as S100B antagonists, thereby presenting novel therapeutic possibilities for epilepsy management. To sum up, this study's deployment of machine learning in conjunction with virtual screening not only has the potential to unearth new epilepsy therapeutics but also underscores the transformative potential of these advanced computational techniques in streamlining and enhancing drug discovery processes.

Natural product rhynchophylline prevents stress-induced hair graying by preserving melanocyte stem cells via the β2 adrenergic pathway suppression

Norepinephrine (NA), a stress hormone, can accelerate hair graying by binding to β2 adrenergic receptors (β2AR) on melanocyte stem cells (McSCs). From this, NA-β2AR axis could be a potential target for preventing the stress effect. However, identifying selective blockers for β2AR has been a key challenge. Therefore, in this study, advanced computer-aided drug design (CADD) techniques were harnessed to screen natural molecules, leading to the discovery of rhynchophylline as a promising compound. Rhynchophylline exhibited strong and stable binding within the active site of β2AR, as verified by molecular docking and dynamic simulation assays. When administered to cells, rhynchophylline effectively inhibited NA-β2AR signaling. This intervention resulted in a significant reduction of hair graying in a stress-induced mouse model, from 28.5% to 8.2%. To gain a deeper understanding of the underlying mechanisms, transcriptome sequencing was employed, which revealed that NA might disrupt melanogenesis by affecting intracellular calcium balance and promoting cell apoptosis. Importantly, rhynchophylline acted as a potent inhibitor of these downstream pathways. In conclusion, the study demonstrated that rhynchophylline has the potential to mitigate the negative impact of NA on melanogenesis by targeting β2AR, thus offering a promising solution for preventing stress-induced hair graying.

Molecular docking-based virtual screening, drug-likeness, and pharmacokinetic profiling of some anti-Salmonella typhimurium cephalosporin derivatives

Objective

The rising cases of resistance to existing antibiotic therapies in Salmonella typhimurium has made it necessary to search for novel drug candidates. The present study employed the molecular docking technique to screen a set of antibacterial cephalosporin analogues against penicillin-binding protein 1a (PBP1a) of the bacterium. This is the first study to screen cephalosporin analogues against PBP1a, a protein central to peptidoglycan synthesis in S. typhimurium.

Methods

Some cephalosporin analogues were retrieved from a drug repository. The structures of the molecules were optimized using the semi-empirical method of Spartan 14 software and were subsequently docked against the active sites of PBP1a using AutoDock vina software. The most potent ligands were chosen as the most promising leads and subsequently subjected to absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiling using the SwissADME online server and DataWarrior chemoinformatics program. The CABSflex 2.0 server was used to carry out molecular dynamics (MD) simulation on the most stable ligand-protein complex.

Results

Compounds 3, 23, and 28 with binding affinity (ΔG) values of -9.2, -8.7, and -8.9 kcal/mol, respectively, were selected as the most promising leads. The ligands bound to the active sites of PBP1a via hydrophobic bonds, hydrogen bonds, and electrostatic interactions. Furthermore, ADMET analyses of the ligands revealed that they exhibited sound pharmacokinetic and toxicity profiles. In addition, an MD study revealed that the most active ligand bound favorably and dynamically to the target protein.

Conclusion

The findings of this research could provide an excellent platform for the discovery and rational design of novel antibiotics against S. typhimurium. Additional in vitro and in vivo studies should be carried out on the drug candidates to validate the findings of this study.

A phenothiazine urea derivative broadly inhibits coronavirus replication via viral protease inhibition

Coronavirus (CoV) replication requires efficient cleavage of viral polyproteins into an array of non-structural proteins involved in viral replication, organelle formation, viral RNA synthesis, and host shutoff. Human CoVs (HCoVs) encode two viral cysteine proteases, main protease (Mpro) and papain-like protease (PLpro), that mediate polyprotein cleavage. Using a structure-guided approach, a phenothiazine urea derivative that inhibits both SARS-CoV-2 Mpro and PLpro protease activity was identified. In silico docking studies also predicted the binding of the phenothiazine urea to the active sites of structurally similar Mpro and PLpro proteases from distantly related alphacoronavirus, HCoV-229 E (229 E), and the betacoronavirus, HCoV-OC43 (OC43). The lead phenothiazine urea derivative displayed broad antiviral activity against all three HCoVs tested in cellulo. It was further demonstrated that the compound inhibited 229 E and OC43 at an early stage of viral replication, with diminished formation of viral replication organelles, and the RNAs that are made within them, as expected following viral protease inhibition. These observations suggest that the phenothiazine urea derivative readily inhibits viral replication and may broadly inhibit proteases of diverse coronaviruses.