Integrated machine learning-based virtual screening and biological evaluation for identification of potential inhibitors against cathepsin K.

Cathepsin K is a type of cysteine proteinase that is primarily expressed in osteoclasts and has a key role in the breakdown of bone matrix protein during bone resorption. Many studies suggest that the deficiency of cathepsin K is concomitant with a suppression of osteoclast functioning, therefore rendering the resorptive properties of cathepsin K the most prominent target for osteoporosis. This innovative work has identified a novel anti-osteoporotic agent against Cathepsin K by using a comparison of machine learning and deep learning-based virtual screening followed by their biological evaluation. Out of ten shortlisted compounds, five of the compounds (JFD02945, JFD02944, RJC01981, KM08968 and SB01934) exhibit more than 50% inhibition of the Cathepsin K activity at 0.1 µM concentration and are considered to have a promising inhibitory effect against Cathepsin K. The comprehensive docking, MD simulation, and MM/PBSA investigations affirm the stable and effective interaction of these compounds with Cathepsin K to inhibit its function. Furthermore, the compounds RJC01981, KM08968 and SB01934 are represented to have promising anti-osteoporotic properties for the management of osteoporosis owing to their significantly well predicted ADMET properties.

Exploring host epigenetic enzymes as targeted therapies for visceral leishmaniasis: in silico design and in vitro efficacy of KDM6B and ASH1L inhibitors.

In order to combat various infectious diseases, the utilization of host-directed therapies as an alternative to chemotherapy has gained a lot of attention in the recent past, since it bypasses the existing limitations of conventional therapies. The use of host epigenetic enzymes like histone lysine methyltransferases and lysine demethylases as potential drug targets has successfully been employed for controlling various inflammatory diseases like rheumatoid arthritis and acute leukemia. In our earlier study, we have already shown that the functional knockdown of KDM6B and ASH1L in the experimental model of visceral leishmaniasis has resulted in a significant reduction of organ parasite burden. Herein, we performed a high throughput virtual screening against KDM6B and ASH1L using > 53,000 compounds that were obtained from the Maybridge library and PubChem Database, followed by molecular docking to evaluate their docking score/Glide Gscore. Based on their docking scores, the selected inhibitors were later assessed for their in vitro anti-leishmanial efficacy. Out of all inhibitors designed against KDM6B and ASH1L, HTS09796, GSK-J4 and AS-99 particularly showed promising in vitro activity with IC50 < 5 µM against both extracellular promastigote and intracellular amastigote forms of L. donovani. In vitro drug interaction studies of these inhibitors further demonstrated their synergistic interaction with amphotericin-B and miltefosine. However, GSK-J4 makes an exception by displaying an in different mode of interaction with miltefosine. Collectively, our in silico and in vitro studies acted as a platform to identify the applicability of these inhibitors targeted against KDM6B and ASH1L for anti-leishmanial therapy.

Synergy between human DNA ligase I and topoisomerase 1 unveils new therapeutic strategy for the management of colorectal cancer.

DNA topoisomerase 1 (Topo 1) is a pivotal player in various DNA processes, including replication, repair, and transcription. It serves as a target for anticancer drugs like camptothecin and its derivatives (Topotecan and SN-38/Irinotecan). However, the emergence of drug resistance and the associated adverse effects, such as alopecia, anemia, dyspnea, fever, chills, and painful or difficult urination, pose significant challenges in Topo 1-targeted therapy, necessitating urgent attention. Human DNA Ligase 1 (hLig I), recognized primarily for its role in DNA replication and repair of DNA breaks, intriguingly exhibits a DNA relaxation activity akin to Topo 1. This raised the hypothesis that hLig I might compensate for Topo 1 inhibition, contributing to resistance against Topo 1 inhibitors. To explore this hypothesis, we assessed the efficacy of hLig I inhibition alone and in combination with Topo 1 in cancer cells. As anticipated, the overexpression of hLig I was observed after Topo 1 inhibition in colorectal cancer cells, affirming our hypothesis. Previously identified as an inhibitor of hLig I's DNA relaxation activity, compound 27 (C 27), when combined with Topotecan, demonstrated a synergistic antiproliferative effect on colorectal cancer cells. Notably, cells with downregulated hLig I (via siRNA, inhibitors, or genetic manipulation) exhibited significantly heightened sensitivity to Topotecan. This observation strongly supports the concept that hLig I contribute to resistance against clinically relevant Topo 1 inhibitors in colorectal cancers. In conclusion, our findings offer evidence for the synergistic impact of combining hLig I inhibitors with Topotecan in the treatment of colorectal cancers, providing a promising strategy to overcome resistance to Topo 1 inhibitors.Communicated by Ramaswamy H. Sarma.

Metacaspase (Pf MCA-1) as antimalarial drug target: An in silico approach and their biological validation.

AIMS: Acquired drug resistance of Plasmodium is a global issue for the treatment of malaria. There are various proteases in the genome of Plasmodium falciparum (P. falciparum) including metacaspase-1 (PfMCA-1) that are essential and are being considered as an attractive drug target. It is aimed to identify novel therapeutics against malaria and their action on PfMCA-1 along with other apoptotic pathway events. MAIN METHODS: High throughput virtual screening of 55,000 compounds derived from Maybridge library was performed against PfMCA-1. Based on the docking score, sixteen compounds were selected for in vitro antimalarial screening against drug sensitive and resistant strains of P. falciparum using SYBR green-based assay. Subsequently, three lead molecules were selected and subjected to the evaluation of cytotoxicity, caspase like protease activity, mitochondrial membrane potential, ROS generation and DNA fragmentation via TUNEL assay. KEY FINDINGS: The in silico and in vitro approaches have brought forward some Maybridge library compounds with antiplasmodial activity most likely by enhancing the metacaspase activity. The compound CD11095 has shown better antimalarial efficacy, and KM06591 depicted higher caspase mediated killing, elevated TUNEL positive cells and moderate ROS generation. Mitochondrial membrane depolarization was augmented by RJC0069. Exposure of P. falciparum to CD11095, KM06591 and RJC0069 has ended up in parasite growth arrest via multiple mechanisms. SIGNIFICANCE: It is proposed that the Maybridge molecules CD11095, KM06591 and RJC0069 have antimalarial activity. Their mechanism of action was found to be by enhancing the metacaspases-like protease activity, mitochondrial depolarization and DNA fragmentation which stipulates significant insights towards promising candidates for drug development.

Identification of potent inhibitors for Leishmania donovani homoserine kinase: an integrated in silico and kinetic study.

Leishmaniasis is caused by ∼20 species of Leishmania that affects millions in endemic areas. Available therapies are not sufficient to effectively control the disease, cause severe side effects and eventually lead to drug resistance, making the discovery of novel therapeutic molecules an immediate need. Molecular target-based drug discovery, where the target is a defined molecular gene, protein or a mechanism, is a rationale driven approach for novel therapeutics. Humans obtain the essential amino acid such as threonine from dietary sources, while Leishmania synthesize it de-novo. Enzymes of the threonine biosynthesis pathway, including the rate limiting Homoserine kinase (HSK) which converts L-homoserine into ortho-phospho homoserine are thus attractive targets for rationale driven therapy. The absence of HSK in humans and its presence in Leishmania donovani enhances the opportunity to exploit HSK as a molecular target for anti-leishmanials therapeutic development. In this study, we utilize structure-based high throughput drug discovery (SBDD), followed by biochemical validation and identified two potential inhibitors (RH00038 and S02587) from Maybridge chemical library that targets L. donovani HSK. These two inhibitors effectively induced the mortality of Leishmania donovani in both amastigote and promastigote stages, with one of them being specific to parasite and twice as effective as the standard therapeutic molecule.

Identification of Potential Aldose Reductase Inhibitors Using Convolutional Neural Network-Based in Silico Screening.

Aldose reductase (ALR2) is a notable enzyme of the polyol pathway responsible for aggravating diabetic neuropathy complications. The first step begins when it catalyzes the reduction of glucose to sorbitol with NADPH as a coenzyme. Elevated concentrations of sorbitol damage the tissues, leading to complications like neuropathy. Though considerable effort has been pushed toward the successful discovery of potent inhibitors, its discovery still remains an elusive task. To this end, we present a 3D convolutional neural network (3D-CNN) based ALR2 inhibitor classification technique by dealing with snapshots of images captured from 3D chemical structures with multiple rotations as input data. The CNN-based architecture was trained on the 360 sets of image data along each axis and further prediction on the Maybridge library by each of the models. Subjecting the retrieved hits to molecular docking leads to the identification of the top 10 molecules with high binding affinity. The hits displayed a better blood-brain barrier penetration (BBB) score (90% with more than four scores) as compared to standard inhibitors (38%), reflecting the superior BBB penetrating efficiency of the hits. Followed by molecular docking, the biological evaluation spotlighted five compounds as promising ALR2 inhibitors and can be considered as a likely prospect for further structural optimization with medicinal chemistry efforts to improve their inhibition efficacy and consolidate them as new ALR2 antagonists in the future. In addition, the study also demonstrated the usefulness of scaffold analysis of the molecules as a method for investigating the significance of structurally diverse compounds in data-driven studies. For reproducibility and accessibility purposes, all of the source codes used in our study are publicly available.

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.

Identification of novel TACE inhibitors using DNN based- virtual screening, molecular dynamics and biological evaluation.

Rheumatoid Arthritis (RA) is a well-known autoimmune inflammatory disease, distressing roughly 1% of the adult population throughout the globe. Many studies have suggested that overexpression of TNF-α, a pro-inflammatory cytokine, is responsible for the progression of RA. Furthermore, inhibition of the shedding rate of TNF-α is regulated by the TACE (TNF-α converting enzyme) protein and, hence is considered as an important therapeutic target for the prevention of progressive synovial joint destruction in rheumatoid arthritis. In the present study, we have proposed a deep neural network (DNN)-based workflow for the virtual screening of compounds towards the identification of potential inhibitors against the TACE proteins. Subsequently, a set of compounds were shortlisted, based on the molecular docking and subjected to the biological evaluation to validate the inhibitory activities of the screened compounds, determine the practical applicability of the DNN-based model, and strengthen the hypothesis. Out of seven, three compounds (BTB10246, BTB10247, and BTB10245) showed significant inhibition at 10 µM and 0.1 µM concentration. These three compounds also showed a stable and significant interaction potential against the TACE protein as compared with the re-docked complex system and can serve as a novel scaffold for further design of new molecules with improved inhibitory activities against TACE.Communicated by Ramaswamy H. Sarma.

Pancreastatin inhibitor PSTi8 ameliorates streptozotocin-induced diabetes by suppressing hepatic glucose production.

Elevated plasma glucose concentration, as a consequence of excessive hepatic glucose production, plays a pivotal role in the development of diabetes. A chromogranin A-derived diabetogenic peptide Pancreastatin (PST) enhances hepatic glucose output leading to diabetes. Therefore, here we probed the role of PSTi8, a PST inhibitor in ameliorating diabetes by investigating the effect of high glucose (HG) or PST on glucose metabolism. Further, we also explored the action mechanism of the underlying anti-hyperglycemic effect of PSTi8. PSTi8 treatment rescue cultured L6 and HepG2 cells from HG and PST-induced insulin resistance, respectively. It also enhances insulin receptor kinase activity by interacting with the insulin receptor and enhancing GLUT4 translocation and glucose uptake. Thus, our in-silico and in-vitro data support the PST-dependent and independent activity of PSTi8. Additionally, PSTi8 treatment in streptozotocin-induced diabetic rats improved glucose tolerance by lowering blood glucose and plasma PST levels. Concomitantly, the treated animals exhibited reduced hepatic glucose production accompanied by downregulation of hepatic gluconeogenic genes PEPCK and G6Pase. PSTi8-treated rats also exhibited enhanced hepatic glycogen in line with reduced plasma glucagon concentrations. Consistently, improved plasma insulin levels in PSTi8-treated rats enhanced skeletal muscle glucose disposal via enhanced P-Akt expression. In summary, these findings suggest PSTi8 has anti-hyperglycemic properties with enhanced skeletal muscle glucose disposal and reduced hepatic gluconeogenesis both PST dependent as well as independent.

An in-silico insight into the predictive interaction of Apolipoprotein-E with Epstein-Barr virus proteins and their probable role in mediating Alzheimer's disease.

Recent reports suggest that persistent Epstein-Barr virus (EBV) infection and its recurrent reactivation could instigate the formation of proteinaceous plaques in the brain: a hallmark of Alzheimer's disease (AD). Interestingly, a major genetic risk factor of AD, the apolipoprotein E (ApoE), could also influence the outcome of EBV infection in an individual. The ApoE is believed to influence the proteinaceous plaque clearance from the brain, and its defective functioning could result in the aggregate deposition. The persistent presence of EBV infection in a genetically predisposed individual could create a perfect recipe for severe neurodegenerative consequences. Therefore, in the present study, we investigated the possible interactions between ApoE and various EBV proteins using computational tools. Our results showed possibly stable de-novo interactions between the C-terminal domain of ApoE3 and EBV proteins: EBV nuclear antigen-1 (EBNA1) and BamHI Z fragment leftward open reading frame-1 (BZLF1). The EBNA1 protein of EBV plays a crucial role in establishing latency and replication of the virus. Whereas BZLF1 is involved in the lytic replication cycle. The proposed interaction of EBV proteins at the ligand-binding site of ApoE3 on CTD could interfere with- its capability to sequester amyloid fragments and, hence their clearance from the brain giving rise to AD pathology. This study provides a new outlook on EBV's underexplored role in AD development and paves the way for novel avenues of investigation which could further our understanding of AD pathogenesis.Communicated by Ramaswamy H. Sarma[Figure: see text].

Artificial neural network models driven novel virtual screening workflow for the identification and biological evaluation of BACE1 inhibitors.

Beta-site amyloid-ß precursor protein-cleaving enzyme 1 (BACE1) is a transmembrane aspartic protease and has shown potential as a possible therapeutic target for Alzheimer's disease. This aggravating disease involves the aberrant production of ß amyloid plaques by BACE1 which catalyzes the rate-limiting step by cleaving the amyloid precursor protein (APP), generating the neurotoxic amyloid ß protein that aggregates to form plaques leading to neurodegeneration. Therefore, it is indispensable to inhibit BACE1, thus modulating the APP processing. In this study, we present a workflow that utilizes a multi-stage virtual screening protocol for identifying potential BACE1 inhibitors by employing multiple artificial neural network-based models. Collectively, all the hyperparameter tuned models were assigned a task to virtually screen Maybridge library, thus yielding a consensus of 41 hits. The majority of these hits exhibited optimal pharmacokinetic properties confirmed by high central nervous system multiparameter optimization (CNS-MPO) scores. Further shortlisting of 8 compounds by molecular docking into the active site of BACE1 and their subsequent in-vitro evaluation identified 4 compounds as potent BACE1 inhibitors with IC50 values falling in the range 0.028-0.052 µM and can be further optimized with medicinal chemistry efforts to improve their activity.

Neohesperidin and spike RBD interaction in omicron and its sub-variants: In silico, structural and simulation studies.

COVID-19 caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged first around December 2019 in the city of Wuhan, China. Since then, several variants of the virus have emerged with different biological properties. This pandemic has so far led to widespread infection cycles with millions of fatalities and infections globally. In the recent cycle, a new variant omicron and its three sub-variants BA.1, BA.2 and BA.3 have emerged which seems to evade host immune defences and have brisk infection rate. Particularly, BA.2 variant has shown high transmission rate over BA.1 strain in different countries including India. In the present study, we have evaluated a set of eighty drugs/compounds using in silico docking calculations in omicron and its variants. These molecules were reported previously against SARS-CoV-2. Our docking and simulation analyses suggest differences in affinity of these compounds in omicron and BA.2 compared to SARS-CoV-2. These studies show that neohesperidin, a natural flavonoid found in Citrus aurantium makes a stable interaction with spike receptor domain of omicron and BA.2 compared to other variants. Free energy binding analyses further validates that neohesperidin forms a stable complex with spike RBD in omicron and BA.2 with a binding energy of -237.9 ± 18.7 kJ/mol and -164.1 ± 17.5 kJ/mol respectively. Key residual differences in the RBD interface of these variants form the basis for differential interaction affinities with neohesperidin as drug binding site overlaps with RBD-human ACE2 interface. These data might be useful for the design and development of novel scaffolds and pharmacophores to develop specific therapeutic strategies against these novel variants.

Ligand-based in silico identification and biological evaluation of potential inhibitors of nicotinamide N-methyltransferase.

Nicotinamide N-methyltransferase (NNMT) is a protein coding gene, which methylates the nicotinamide (NA) (vitamin B3) to produce 1-methylnicotinamide (MNA). Several studies have suggested that the overexpression of NNMT is associated with different metabolic disorders like obesity and type-2 diabetes thereby making it an important therapeutic target for development of anti-diabetic agents. Here we describe a workflow for identification of new inhibitors of NNMT from a library of small molecules. In this study, we have hypothesized a four-point pharmacophore model based on the pharmacophoric features of reported NNMT inhibitors in the literature. The statistically significant pharmacophore hypothesis was used to explore the Maybridge compound library that resulted in mapping of 1330 hit compounds on the proposed hypothesis. Subsequently, a total of eight high scoring compounds, showing good protein-ligand interactions in the molecular docking study, were selected for biological evaluation of NNMT activity. Eventually, four compounds were found to show significant inhibitory activity for NNMT and can be further explored to design new derivatives around the identified scaffolds with improved activities as NNMT inhibitors.

Machine learning-based predictive modeling, virtual screening and biological evaluation studies for identification of potential inhibitors of MMP-13.

Matrix Metalloproteinase-13 (MMP-13) is a collagenase that regulates the homeostasis of the extracellular matrix (ECM) and basement membrane, as well as the breakdown of type II collagen. Recent research studies on the molecular and cellular mechanisms of cartilage degradation suggest that MMP-13 overexpression triggers osteoarthritis and is considered a promising target for osteoarthritis treatment. The present work employs machine learning-based virtual screening and structure-based rational drug design approaches to identify potential inhibitors of MMP-13 with diverse chemical scaffolds. The twelve top-scoring screened compounds were subjected to biological evaluation to validate the robustness and predictive modeling of ML-based Virtual Screening. It was observed that eight compounds exhibited approximately 44%-60% inhibition at 0.1 µM concentration, and the IC50 lies in the range of 1.9-2.3 µM against MMP-13. Interestingly, two of the compounds, DP01473 and RH01617, showed potent dose-dependent inhibitory activity. Compound DP01473 inhibited MMP-13 by 44%, 50%, and 70%, while compound RH01617 inhibited MMP-13 by 54%, 55%, and 57% at 0.1 µM, 1 µM, and 10 µM concentrations, respectively, and can be further optimized for the design and development of more potent MMP-13 inhibitors.Communicated by Ramaswamy H. Sarma.

10-Residue MyD88-Peptide Adopts ß-Sheet Structure, Self-Assembles, Binds to Lipopolysaccharides, and Rescues Mice from Endotoxin-Mediated Lung-Infection and Death.

Naturally occurring cationic antimicrobial peptides (AMPs) mostly adopt α-helical structures in bacterial membrane mimetic environments. To explore the design of novel ß-sheet AMPs, we identified two short cationic amphipathic ß-strand segments from the crystal structure of the innate immune protein, MyD88. Interestingly, of these, the 10-residue arginine-valine-rich synthetic MyD88-segment, KRCRRMVVVV (M3), exhibited ß-sheet structure when bound to the outer membrane Gram-negative bacterial component, LPS. Isothermal titration calorimetric data showed that M3 bound to LPS with high affinity, and the interaction was hydrophobic in nature. Supporting these observations, computational studies indicated strong interactions of multiple and consecutive valine residues of M3 with the acyl chain of LPS. Moreover, M3 adopted nanosheet and nanofibrillar structure in 25% acetonitrile/water and isopropanol, respectively. M3 showed substantial antibacterial activities against both Gram-positive and Gram-negative bacteria which it appreciably retained in the presence of human serum and physiological salts. M3 was non-hemolytic against human red blood cells and non-cytotoxic to 3T3 cells up to 200 µM and to mice in vivo at a dose of 40 mg/kg. Furthermore, M3 neutralized LPS-induced pro-inflammatory responses in THP-1 cells and rat bone marrow-derived macrophages. Consequently, M3 attenuated LPS-mediated lung inflammation in mice and rescued them (80% survival at 10 mg/kg dose) against a lethal dose of LPS. The results demonstrate the identification of a 10-mer LPS-interacting, ß-sheet peptide from MyD88 with the ability to form nanostructures and in vivo activity against LPS challenge in mice. The identified M3-template provides scope for designing novel bioactive peptides with ß-sheet structures and self-assembling properties.

Design, synthesis and biological evaluation of (Quinazoline 4-yloxy)acetamide and (4-oxoquinazoline-3(4H)-yl)acetamide derivatives as inhibitors of Mycobacterium tuberculosis bd oxidase.

New chemical scaffolds with novel mechanism of action are urgently needed for the treatment of drug resistant tuberculosis. The oxidative phosphorylation pathway of Mycobacterium tuberculosis consists of multiple clinically validated drug targets. This pathway can function through any one of the two terminal oxidases-the proton pumping cytochrome bc1-aa3 supercomplex, or the less energy efficient but high affinity cytochrome bd oxidase. Inhibiting the bc1 complex alone has been found bacteriostatic and not bactericidal. On the other hand, inhibition of both these oxidases turns lethal to the pathogen. In the present study, we used a bc1 complex mutant of M. tuberculosis to screen (Quinazoline 4-yloxy)acetamide and (4-oxoquinazoline-3(4H)-yl)acetamide derivatives against the alternate oxidase, i.e., cytochrome bd oxidase. Two molecules, S-021-0601 and S-021-0607 were found to inhibit the mutant with MICs 8 and 16 µM respectively, compared to MICs of 128 and 256 µM against the wild type M. tuberculosis. In the wild type, one of the compounds showed synergism with Q203, an inhibitor of bc1 complex, in inhibiting growth under aerobic conditions. Both compounds showed synergism with Q203 in depleting bacterial ATP and inhibiting oxygen consumption. Both the compounds at 32 µM (one-fourth or one-eighth of their MICs for wild type) were bactericidal to wild type bacteria under hypoxic condition, causing ∼1.9 log10 reduction in viable counts which increased to ∼4-log10 when combined with Q203.

Integrated support vector machine and pharmacophore based virtual screening driven identification of thiophene carboxamide scaffold containing compound as potential PARP1 inhibitor.

Poly (ADP-ribose) polymerase-1 (PARP1) inhibition strategy for cancer treatment is gaining advantage particularly in patients having a mutation in BRCA1/BRCA2 gene. To date, four drugs have obtained FDA approval and some inhibitors are in clinical trials. To identify more potent PARP1 inhibitors extensive research is going on to enrich the library of PARP1 inhibitors with compounds belonging to different classes. We employed an integrated virtual screening approach to identify potential PARP1 inhibitors. The sequential support vector machine (SVM) and pharmacophore model based virtual screening was carried out on the Maybridge library. The obtained hits were docked in the binding site of the PARP1 catalytic domain and nine drug-like compounds showing good ADME properties and form critical molecular interactions with the binding site residues were considered for the in vitro PARP1 inhibition assay. MD simulations were performed to decipher the stability of the PARP1-ligand complexes. Hydrogen bond interactions were also probed for their stability during MD simulations. We have identified three compounds (BTB02767, GK01172, and KM09200) showing 50% inhibition of PARP1 enzyme activity at 25 µM. BTB02767 and KM09200 have phthalazinone scaffold, while GK01172 bears a thiophene carboxamide scaffold, which could be a new chemotype of PARP1 inhibitors. In conclusion, GK01172 may serve as an important compound for further development of PARP1 inhibitors containing thiophene carboxamide scaffold.Communicated by Ramaswamy H. Sarma.

Deep neural network modeling based virtual screening and prediction of potential inhibitors for renin protein.

Renin enzyme plays an essential role in the Renin-Angiotensin System (RAS), and it is involved in the pathogenesis of hypertension and several other cardiovascular diseases (CVDs). Inhibition of renin is an effective way to intervene with the pathogenesis of these diseases. Docking-based virtual screening, 3D-Quantitative Structure-Activity Relationship (3D-QSAR), and structure-based drug design are the most frequently used strategies towards discovering novel inhibitors targeting renin. In this study, we have developed a 2D fingerprint-based Deep Neural Network (DNN) classifier for virtual screening and a DNN-QSAR model for biological activity prediction. The resulting hits from the DNN-QSAR model were then subjected to the molecular docking to identify further top hits. Molecular Dynamics (MD) simulation was conducted to get a better insight into the binding mode of identified hits. We have discovered six compounds from the Maybridge chemical database with the predicted IC50 values ranging from 24.2 nM to 83.6 nM. To the best of our knowledge, this is the first study that used a cascaded DNN model to identify potential lead compounds for the inhibition of renin target. Through the results presented in this study, we provide evidence of the DNN method being a useful approach to identify new chemical entities/novel lead compounds that may overcome the limitation of existing conventional strategies used in drug discovery research.Communicated by Ramaswamy H. Sarma.

Synthesis and Evaluation of Galloyl Conjugates of Flavanones as BMP-2 Upregulators with Promising Bone Anabolic and Fracture Healing Properties.

The molecular hybridization concept led us to design a series of galloyl conjugates of flavanones that have potent osteoblast differentiation ability in vitro and promote bone formation in vivo. An array of in vitro studies, especially gene expression of osteogenic markers, evinced compound 5e as the most potent bone anabolic agent, found to be active at 1 pM, which was then further assessed for its osteogenic potential in vivo. From in vivo studies on rat calvaria and a fracture defect model, we inferred that compound 5e, at an oral dose of 5 mg/(kg day), increased the expression of osteogenic genes (RUNX2, BMP-2, Col1, and OCN) and the bone formation rate and significantly promoted bone regeneration at the fracture site, as evidenced by the increased bone volume/tissue fraction compared with vehicle-treated rats. Furthermore, structure-activity relationship studies and pharmacokinetic studies suggest 5e as a potential bone anabolic lead for future osteoporosis drug development.

Recent trends in artificial intelligence-driven identification and development of anti-neurodegenerative therapeutic agents.

Neurological disorders affect various aspects of life. Finding drugs for the central nervous system is a very challenging and complex task due to the involvement of the blood-brain barrier, P-glycoprotein, and the drug's high attrition rates. The availability of big data present in online databases and resources has enabled the emergence of artificial intelligence techniques including machine learning to analyze, process the data, and predict the unknown data with high efficiency. The use of these modern techniques has revolutionized the whole drug development paradigm, with an unprecedented acceleration in the central nervous system drug discovery programs. Also, the new deep learning architectures proposed in many recent works have given a better understanding of how artificial intelligence can tackle big complex problems that arose due to central nervous system disorders. Therefore, the present review provides comprehensive and up-to-date information on machine learning/artificial intelligence-triggered effort in the brain care domain. In addition, a brief overview is presented on machine learning algorithms and their uses in structure-based drug design, ligand-based drug design, ADMET prediction, de novo drug design, and drug repurposing. Lastly, we conclude by discussing the major challenges and limitations posed and how they can be tackled in the future by using these modern machine learning/artificial intelligence approaches.