Aldose reductase inhibitory and antiglycation properties of phytoconstituents of Cichorium intybus: Potential therapeutic role in diabetic retinopathy

Diabetic vascular complication including diabetic retinopathy is a major morbidity in Saudia Arabia. The polyol pathway aka aldose reductase (AR) pathway has gained significant association with diabetic retinopathy with regard to chronically enhanced glucose metabolism. Considerable research has been put forth to develop more effective therapeutic strategies to overcome the overwhelming challenges of vascular complications associated with diabetes. In this regard, constituents of Cichorium intybus can offer strong AR inhibitory potential because of their strong antidiabetic properties. Therefore, aim of this study was to investigate the AR inhibitory as well as antiglycation potential of C. intybus extract/compounds. The preliminary in vitro results showed that methanolic extract of C. intybus could significantly inhibit AR enzyme and advanced glycation end product formation. Eventually, based on previous studies and reviews, we selected one hundred fifteen C. intybus root constituents and screened them through Lipinski's rule of five and ADMET analysis. Later, after molecular docking analysis of eight compounds, five best were selected for molecular dynamics simulation to deduce their binding affinity with the AR enzyme. Finally, three out of five compounds were further tested in vitro for their AR inhibitory potential and antiglycation properties. Enzyme assay and kinetic studies showed that all the three tested compounds were having potent AR inhibitory properties, although to a lesser extent than ellagic acid and tolrestat. Similarly, kaempferol showed strong antiglycation property equivalent to ellagic acid, but greater than aminoguanidine. Intriguingly, significant reduction in sorbitol accumulation in RBCs by the tested compounds substantiated strong AR inhibition by these compounds. Moreover, decrease in sorbitol accumulation under high glucose environment also signifies the potential application of these compounds in diabetic retinopathy and other vascular complications. Thus, in sum, the in silico and in vitro studies combinedly showed that C. intybus root is a treasure for therapeutic compounds and can be explored further for drug development against diabetic retinopathy.

Binding studies of potential amyloid-β inhibiting chalcone derivative with bovine serum albumin

Chalcones (α-phenyl-β-benzoylethylene) and their natural-source derivatives have been investigated for their remarkable biological activities, like neuroprotective, anti-inflammatory, and anti-tumor properties. A triazole chalcone ligand (E)-3-(4-(dimethylamino)phenyl)-1-(4-((1-(2-(4-((E)-3-(4(dimethylamino)phenyl)acryloyl)phenoxy)ethyl)-1H-1,2,3-triazol-4-yl)methoxy)phenyl)prop-2-en-1-one (L1) was synthesized by Cu(I)- catalysed click reaction. The mechanistic properties of L1 for therapy were evaluated by analyzing the binding interactions between L1 and bovine serum albumin (BSA) through photophysical and computational studies. The structural elucidation of ligand L1 was carried out by NMR and mass spectrometry. The Aβ inhibitory activity of L1 was studied by thioflavin T assay and transmission electron microscopy. The biomolecular interaction of L1 with bovine serum albumin was examined through multi-spectroscopic techniques in combination with in silico studies. UV-Visible absorption, fluorescence spectroscopy, circular dichroism, Förster resonance energy transfer, and three-dimensional fluorescence studies confirmed the formation of a BSA-L1 complex. The potential binding sites, mechanism of interactions, and variations in the environment of tyrosine and tryptophan amino acid residues of BSA were assessed at different temperatures. The binding constant for the Static quenching mechanism of intrinsic fluorescence of BSA was of the order of 105 M-1. The esterase enzyme activity assay in the presence of L1 revealed an increase in the protein enzyme activity. Molecular docking studies suggested L1 was predominantly bound to BSA by hydrogen bonds and Van der Waals forces.

Screening of phytochemicals from Clerodendrum inerme (L.) Gaertn as potential anti-breast cancer compounds targeting EGFR: an in-silico approach

Breast cancer (BC) is the most prevalent malignancy among women around the world. The epidermal growth factor receptor (EGFR) is a tyrosine kinase receptor (RTK) of the ErbB/HER family. It is essential for triggering the cellular signaling cascades that control cell growth and survival. However, perturbations in EGFR signaling lead to cancer development and progression. Hence, EGFR is regarded as a prominent therapeutic target for breast cancer. Therefore, in the current investigation, EGFR was targeted with phytochemicals from Clerodendrum inerme (L.) Gaertn (C. inerme). A total of 121 phytochemicals identified by gas chromatography-mass spectrometry (GC-MS) analysis were screened against EGFR through molecular docking, ADMET analyses (Absorption, Distribution, Metabolism, Excretion, and Toxicity), PASS predictions, and molecular dynamics simulation, which revealed three potential hit compounds with CIDs 10586 [i.e. alpha-bisabolol (-6.4 kcal/mol)], 550281 [i.e. 2,(4,4-Trimethyl-3-hydroxymethyl-5a-(3-methyl-but-2-enyl)-cyclohexene) (-6.5 kcal/mol)], and 161271 [i.e. salvigenin (-7.4 kcal/mol)]. The FDA-approved drug gefitinib was used to compare the inhibitory effects of the phytochemicals. The top selected compounds exhibited good ADMET properties and obeyed Lipinski's rule of five (ROF). The molecular docking analysis showed that salvigenin was the best among the three compounds and formed bonds with the key residue Met 793. Furthermore, the molecular mechanics generalized born surface area (MMGBSA) calculations, molecular dynamics simulation, and normal mode analysis validated the binding affinity of the compounds and also revealed the strong stability and compactness of phytochemicals at the docked site. Additionally, DFT and DOS analyses were done to study the reactivity of the compounds and to further validate the selected phytochemicals. These results suggest that the identified phytochemicals possess high inhibitory potential against the target EGFR and can treat breast cancer. However, further in vitro and in vivo investigations are warranted towards the development of these constituents into novel anti-cancer drugs.Communicated by Ramaswamy H. Sarma.

Selective inhibitors targeting Fis1/Mid51 protein-protein interactions protect against hypoxia-induced damage in cardiomyocytes

Cardiovascular diseases (CVDs) are the most common non-communicable diseases globally. An estimated 17.9 million people died from CVDs in 2019, representing 32% of all global deaths. Mitochondria play critical roles in cellular metabolic homeostasis, cell survival, and cell death, as well as producing most of the cell's energy. Protein-protein interactions (PPIs) have a significant role in physiological and pathological processes, and aberrant PPIs are associated with various diseases, therefore they are potential drug targets for a broad range of therapeutic areas. Due to their ability to mimic natural interaction motifs and cover relatively larger interaction region, peptides are very promising as PPI inhibitors. To expedite drug discovery, computational approaches are widely used for screening potential lead compounds. Here, we developed peptides that inhibit mitochondrial fission 1 (Fis1)/mitochondrial dynamics 51 kDa (Mid51) PPI to reduce the cellular damage that can lead to various human pathologies, such as CVDs. Based on a rational design approach we developed peptide inhibitors of the Fis1/Mid51 PPI. In silico and in vitro studies were done to evaluate the biological activity and molecular interactions of the peptides. Two peptides, CVP-241 and CVP-242 were identified based on low binding energy and molecular dynamics simulations. These peptides inhibit Fis1/Mid51 PPI (-1324.9 kcal mol-1) in docking calculations (CVP-241, -741.3 kcal mol-1, and CVP-242, -747.4 kcal mol-1), as well as in vitro experimental studies Fis1/Mid51 PPI (KD 0.054 µM) Fis1/Mid51 PPI + CVP-241 (KD 3.43 µM), and Fis1/Mid51 PPI + CVP-242 (KD 44.58 µM). Finally, these peptides have no toxicity to H9c2 cells, and they increase cell viability in cardiomyocytes (H9c2 cells). Consequently, the identified inhibitor peptides could serve as potent molecules in basic research and as leads for therapeutic development.

Therapeutic Study of Cinnamic Acid Derivative for Oxidative Stress Ablation: The Computational and Experimental Answers

This study aimed to examine the therapeutic activity of the cinnamic acid derivative KAD-7 (N'-(2,4-dichlorobenzylidene)-3-(4-methoxyphenyl) acrylohydrazide) on Fe2+-induced oxidative hepatic injury via experimental and computational models. In addition, the role of ATPase and ectonucleoside triphosphate diphosphohydrolase (ENTPDase) in the coordination of cellular signals is speculated upon to proffer suitable therapeutics for metabolic stress disorder upon their inhibition. While we know little about therapeutics with flexible dual inhibitors for these protein targets, this study was designed to screen KAD-7's (N'-(2,4-dichlorobenzylidene)-3-(4-methoxyphenyl) acrylohydrazide) inhibitory potential for both protein targets. We induced oxidative hepatic damage via the incubation of hepatic tissue supernatant with 0.1 mM FeSO4 for 30 min at 37 °C. We achieved the treatment by incubating the hepatic tissues with KAD-7 under the same conditions. The catalase (CAT), glutathione (GSH), malondialdehyde (MDA), ATPase, and ENTPDase activity were all measured in the tissues. We predicted how the drug candidate would work against ATPase and ENTPDase targets using molecular methods. When hepatic injury was induced, there was a significant decrease in the levels of the GSH, CAT, and ENTPDase (p < 0.05) activities. In contrast, we found a noticeable rise in the MDA levels and ATPase activity. KAD-7 therapy resulted in lower levels of these activities overall (p < 0.05), as compared to the control levels. We found the compound to have a strong affinity for ATPase (-7.1 kcal/mol) and ENTPDase (-7.4 kcal/mol), and a better chemical reactivity than quercetin. It also met all drug-likeness parameters. Our study shows that KAD-7 can protect the liver from damage caused by FeSO4 by reducing oxidative stress and purinergic actions. Our studies indicate that KAD-7 could be developed as a therapeutic option since it can flexibly inhibit both ATPase and ENTPDase.

Computer-aided analysis for identification of novel analogues of ketoprofen based on molecular docking, ADMET, drug-likeness and DFT studies for the treatment of inflammation

Computer-based drug design is increasingly used in strategies for discovering new molecules for therapeutic purposes. The targeted drug is ketoprofen (KTP), which belongs to the family of non-steroidal anti-inflammatory drugs, which are widely used for the treatment of pain, fever, inflammation and certain types of cancers. In an attempt to rationalize the search for 72 new potential anti-inflammatory compounds on the COX-2 enzyme, we carried out an in silico protocol that successfully combines molecular docking towards COX-2 receptor (5F1A), ADMET pharmacokinetic parameters, drug-likeness rules and molecular electrostatic potential (MEP). It was found that six of the compounds analyzed satisfy with the associated values to physico-chemical properties as key evaluation parameters for the drug-likeness and demonstrate a hydrophobic character which makes their solubility in aqueous media difficult and easy in lipids. All the compounds presented good ADMET profile and they showed an interaction with the amino acids responsible for anti-inflammatory activity of the COX-2 isoenzyme. The calculation of the MEP of the six analogues reveals new preferential sites involving the formation of new bonds. Consequently, this result allowed us to understand the origin of the potential increase in the anti-inflammatory activity of the candidates. Finally, it was obtained that six compounds have a binding mode, binding energy, and stability in the active site of COX-2 like the reference drug ketoprofen, suggesting that these compounds could become a powerful candidate in the inhibition of the COX-2 enzyme.Communicated by Ramaswamy H. Sarma.

In Vitro and In Silico Analysis of the Anticancer Effects of Eurycomanone and Eurycomalactone from Eurycoma longifolia

Eurycomanone and eurycomalactone are known quassinoids present in the roots and stems of Eurycoma longifolia. These compounds had been reported to have cytotoxic effects, however, their mechanism of action in a few cancer cell lines have yet to be elucidated. This study was aimed at investigating the anticancer effects and mechanisms of action of eurycomanone and eurycomalactone in cervical (HeLa), colorectal (HT29) and ovarian (A2780) cancer cell lines via Sulforhodamine B assay. Their mechanism of cell death was evaluated based on Hoechst 33342 assay and in silico molecular docking toward DHFR and TNF-α as putative protein targets. Eurycomanone and eurycomalactone exhibited in vitro anticancer effects manifesting IC50 values of 4.58 ± 0.090 µM and 1.60 ± 0.12 µM (HeLa), 1.22 ± 0.11 µM and 2.21 ± 0.049 µM (HT-29), and 1.37 ± 0.13 µM and 2.46 ± 0.081 µM (A2780), respectively. They induced apoptotic cancer cell death in dose- and time-dependent manners. Both eurycomanone and eurycomalactone were also predicted to have good inhibitory potential as demonstrated by the docking into TNF-α with binding affinity of -8.83 and -7.51 kcal/mol, respectively, as well as into DHFR with binding affinity results of -8.05 and -8.87 kcal/mol, respectively. These results support the evidence of eurycomanone and eurycomalactone as anticancer agents via apoptotic cell death mechanism that could be associated with TNF-α and DHFR inhibition as among possible protein targets.

Identification of in vitro metabolites of an Allium organosulfur compound and environmental toxicity prediction as part of its risk assessment

Propyl-propane-thiosulfonate (PTSO) is an organosulfur compound found inAllium spp. Due to its antioxidant and antimicrobial activities, PTSO has been proposed for applications in the agri-food sector, such as feed additive. However, its use with commercial purposes depends on its toxicity evaluation. The present work aimed to perform a pilot-study of toxicokinetic profile of PTSO combining in silico and in vitro techniques, important steps in the risk assessment process. In silico ecotoxicity studies were also performed considering the importance of the environmental impact of the compound before its commercial use. First, an analytical method has been developed and validated to determine the original compound and its metabolites by ultra-performance liquid chromatography-tandem mass spectrometry. The phase I and II metabolism of PTSO was predicted using Meta-Pred Web Server. For the phase I metabolism, rat (male and female) and human liver microsomes were incubated with PTSO and NADPH regeneration system. Furthermore, in the phase II, microsomes were incubated with PTSO and glutathione or uridine 5'- diphosphoglucuronic acid. The analysis revealed the presence of propylpropane thiosulfinate (PTS) originated by redox reaction in phase I, and two conjugates from the phase II: S-propylmercaptoglutathione (GSSP) and S-propylmercaptocysteine (CSSP). Additionally, considering the environmental fate of PTSO and its metabolites, the ADME parameters and the potential ecotoxicity were also predicted using in silico softwares. The results of the ecotoxicity in silico study evidenced that the metabolism induced the formation of detoxified metabolites from the parent compound, except for dimercaprol and 3-mercaptopropane1,2-diol. Further in vivo assays are needed to confirm this prediction.

Investigation of Methyl-5-(pentan-3-yloxy)-7-oxabicyclo[4.1.0]hept-3-ene-3-carboxyhydrazide Derivatives as Potential Inhibitors of COVID-19 Main Protease: DFT and Molecular Docking Study

The search for effective therapeutics to combat COVID-19 has led to the exploration of the biological activity of numerous compounds. In this study, hydrazones derived from oseltamivir intermediate, methyl 5-(pentan-3-yloxy)-7-oxabicyclo[4.1.0]hept-3-ene-3-carboxylate have been investigated for their potential as drug candidates against the COVID-19 virus using computational methods, including density functional theory (DFT) studies, molecular docking, and absorption, distribution, metabolism, excretion and toxicity (ADMET) analysis. The DFT studies provide information on the electronic properties of the compounds while the molecular docking results using AutoDock reported the binding energies between the main protease of COVID-19 and the compounds. The DFT results revealed that the energy gap of the compounds ranged from 4.32 to 5.82 eV while compound HC had the highest energy gap (5.82 eV) and chemical potential (2.90 eV). The electrophilicity index values of the 11 compounds ranged from 2.49 to 3.86, thus they were classified as strong electrophiles. The molecular electrostatic potential (MESP) revealed electron-rich and electron-deficient regions of the compounds. The docking results reveal that all the compounds had better docking scores than remdesivir and chloroquine, frontline drugs employed in combating COVID-19, with HC having the best docking score of -6.5. The results were visualized using Discovery studio, which revealed hydrogen bonding, pi-alkyl interaction, alkyl interaction, salt bridge interaction, halogen interaction as being responsible for the docking scores. The drug-likeness results showed that the compounds qualify as oral drug candidates as none of them violated Vebers and Lipinski's rule. Thus, they could serve as potential inhibitors of COVID-19.

In Silico Study of Thiourea Derivatives as Potential Epidermal Growth Factor Receptor Inhibitors

Over the years, the escalation of cancer cases has been linked to the resistance, less selectivity, and toxicity of available anticancer drugs to normal cells. Therefore, continuous efforts are necessary to find new anticancer drugs with high selectivity of epidermal growth factor receptor tyrosine kinase (EGFR-TK) as a therapeutic target. The EGFR-TK protein has a crucial role in cell proliferation and cancer progression. With about 30% of cancer cases involved with the protein, it has piqued the interest as a therapeutic target. The potential of theoretically designed thiourea derivatives as anticancer agents in this report was evaluated against EGFR-TK via in silico techniques, including molecular docking (AutoDock Vina), molecular dynamics simulations (GROMACS), pharmacokinetics, and drug-likeness properties (SwissADME and Molinspiration). New hybrid molecules of the thiourea derivative moiety were designed in this study based on the fragment-based drug discovery and linked with diverse pharmacophoric fragments with reported anticancer potential ([Formula: see text]) and the modification of the methyl position on phenyl ring ([Formula: see text]). These fragments include pyridine, thiophene, furan, pyrrole and styrene groups. Out of 15 compounds, compound 13 displayed the most potent inhibitory activity, with the lowest binding affinity in docking of [Formula: see text]8.7 kcal/mol compared to the positive control erlotinib of [Formula: see text]6.7 kcal/mol. Our molecular dynamics (MD) simulations revealed that molecule 13, comprising styrene and 2-methylphenyl substituents on [Formula: see text] and [Formula: see text], respectively, showed adequate compactness, uniqueness and satisfactory stability. Subsequently, the absorption, distribution, metabolism, excretion and toxicity (ADMET) properties and drug-likeness properties also indicate that this theoretically designed inhibitor ( 13) is less toxic and contains high druggable properties. Thus, compound 13 could be promising against EGFR-TK.

Exploration of anticancer potential of Lantadenes from weed Lantana camara: Synthesis, in silico, in vitro and in vivo studies

Naturally occurring pentacyclic triterpenoids and their semisynthetic analogues have engrossed increasing attention for their anticancer potential and exhibiting promising role in discovery of new anticancer agents. Present study include the semi synthetic modifications of Lantadenes from the weed Lantana carama and their structures delineation by FT-IR, ¹H-NMR, ¹³C-NMR & mass spectroscopy. All the compounds were scrutinized for in vitro cytotoxicity, ligand receptor interaction and in vivo anticancer studies. Most of the novel analogues displayed potent antiproliferative activity against A375 & A431 cancer cell lines and found superior to parent Lantadenes. In particular, 3β-(4-Methoxybenzoyloxy)-22β-senecioyloxy-olean-12-en-28-oic acid was found to be most suitable compound, with IC₅₀ value of 3.027 μM aganist A375 cell line having least docking score (-69.40 kcal/mol). Promising anticancer potential of the lead was further indicated by significant reduction in tumor volume and burden in two stage carcinoma model. These findings suggests that the Lantadene derivatives may hold promising potential for the intervention of skin cancers.

Machine learning and artificial intelligence in physiologically based pharmacokinetic modeling

Physiologically based pharmacokinetic (PBPK) models are useful tools in drug development and risk assessment of environmental chemicals. PBPK model development requires the collection of species-specific physiological, and chemical-specific absorption, distribution, metabolism, and excretion (ADME) parameters, which can be a time-consuming and expensive process. This raises a need to create computational models capable of predicting input parameter values for PBPK models, especially for new compounds. In this review, we summarize an emerging paradigm for integrating PBPK modeling with machine learning (ML) or artificial intelligence (AI)-based computational methods. This paradigm includes 3 steps (1) obtain time-concentration PK data and/or ADME parameters from publicly available databases, (2) develop ML/AI-based approaches to predict ADME parameters, and (3) incorporate the ML/AI models into PBPK models to predict PK summary statistics (eg, area under the curve and maximum plasma concentration). We also discuss a neural network architecture "neural ordinary differential equation (Neural-ODE)" that is capable of providing better predictive capabilities than other ML methods when used to directly predict time-series PK profiles. In order to support applications of ML/AI methods for PBPK model development, several challenges should be addressed (1) as more data become available, it is important to expand the training set by including the structural diversity of compounds to improve the prediction accuracy of ML/AI models; (2) due to the black box nature of many ML models, lack of sufficient interpretability is a limitation; (3) Neural-ODE has great potential to be used to generate time-series PK profiles for new compounds with limited ADME information, but its application remains to be explored. Despite existing challenges, ML/AI approaches will continue to facilitate the efficient development of robust PBPK models for a large number of chemicals.

A Computational Study of Carbazole Alkaloids from Murraya koenigii as Potential SARS-CoV-2 Main Protease Inhibitors

Despite COVID-19 vaccination, immune escape of new SARS-CoV-2 variants has created an urgent priority to identify additional antiviral drugs. Targeting main protease (M^pro) expressed by SARS-CoV-2 is a therapeutic strategy for drug development due to its prominent role in viral replication cycle. Leaves of Murraya koenigii are used in various traditional medicinal applications and this plant is known as a rich source of carbazole alkaloids. Thus, this computational study was designed to investigate the inhibitory potential of carbazole alkaloids from Murraya koenigii against M^pro. Molecular docking was initially used to determine the binding affinity and molecular interactions of carbazole alkaloids and the reference inhibitor (3WL) in the active site of SARS-CoV-2 M^pro (PDB ID: 6M2N).The top scoring compounds were further assessed for protein structure flexibility, physicochemical properties and drug-likeness, pharmacokinetic and toxicity (ADME/T) properties, antiviral activity, and pharmacophore modeling. Five carbazole alkaloids (koenigicine, mukonicine, o-methylmurrayamine A, koenine, and girinimbine) displayed a unique binding mechanism that shielded the catalytic dyad of M^pro with stronger binding affinities and molecular interactions than 3WL. Furthermore, the compounds with high affinity displayed favorable physicochemical and ADME/T properties that satisfied the criteria for oral bioavailability and druggability. The pharmacophore modeling study shows shared pharmacophoric features of those compounds for their biological interaction with M^pro. During the molecular dynamics simulation, the top docking complexes demonstrated precise stability except koenigicine. Therefore, mukonicine, o-methylmurrayamine A, koenine, and girinimbine may have the potential to restrict SARS-CoV-2 replication by inactivating the M^pro catalytic activity.

tLyp-1: A peptide suitable to target NRP-1 receptor

Targeting vascular endothelial growth factor receptor (VEFGR) and its co-receptor neuropilin-1 (NRP-1) is an interesting vascular strategy. tLyp-1 is a tumor-homing and penetrating peptide of 7 amino acids (CGNKRTR). It is a truncated form of Lyp-1 (CGNKRTRGC), which is known to target NRP-1 receptor, with high affinity and specificity. It is mediated by endocytosis via C-end rule (CendR) internalization pathway. The aim of this study is to evaluate the importance of each amino acid in the tLyp-1 sequence through alanine-scanning (Ala-scan) technique, during which each of the amino acid in the sequence was systematically replaced by alanine to produce 7 different analogues. In silico approach through molecular docking and molecular dynamics are employed to understand the interaction between the peptide and its analogues with the NRP-1 receptor, followed by in vitro ligand binding assay study. The C-terminal Arg is crucial in the interaction of tLyp-1 with NRP-1 receptor. Substituting this residue dramatically reduces the affinity of this peptide which is clearly seen in this study. Lys-4 is also important in the interaction, which is confirmed via the in vitro study and the MM-PBSA analysis. The finding in this study supports the CendR, in which the presence of R/K-XX-R/K motif is essential in the binding of a ligand with NRP-1 receptor. This presented work will serve as a guide in the future work pertaining the development of active targeting agent towards NRP-1 receptor.

Anti-Alopecia Activity of Alkaloids Group from Noni Fruit against Dihydrotestosterone-Induced Male Rabbits and Its Molecular Mechanism: In Vivo and In Silico Studies

Androgenic alopecia (AA) is a condition that most commonly affects adult men and is caused by an increase in the hormone dihydrotestosterone (DHT) in the hair follicles. Anti-alopecia drugs should be discovered for hair follicles to enter the anagen growth phase. Therefore, this study evaluated the hair growth-promoting activity of Noni fruit’s water, ethyl acetate, n-hexane fractions, and sub-fractions from the active fraction in the alopecia male white rabbit model. The Matias method was modified by inducing rabbits using DHT for 17 days, followed by topical application of Noni fruit solution for 21 days. Meanwhile, hair growth was evaluated by histological observation of the follicular density and the anagen/telogen (A/T) ratio in skin tissue. In the first stage, five groups of male white rabbits were studied to obtain the active fraction; DHT+Minoxidil as standard, DHT+vehicle (NaCMC 1%), DHT+FW, DHT+FEA, and DHT+FH. The FEA as the active fraction was followed by open-column chromatography separation (DCM:Methanol) with a gradient of 10% to produce sub-fractions. In the second stage, the six main sub-fraction groups of male rabbits studied were DHT+FEA-1 to DHT+FEA-6. The follicular density of groups FEA-3 was 78.00 ± 1.52 compared with 31.55 ± 1.64 and 80.12 ± 1.02 in the Vehicle and Minoxidil groups. Additionally, group FEA-3 showed large numbers of anagen follicles with an A/T ratio of 1.64/1 compared to the vehicle group of 1/1.50 and 1.39/1 for Minoxidil control. Group FEA-3 was identified by LC-MS/MS-QTOF, followed by molecular docking to the androgen receptor (PDB: 4K7A), causing alopecia. The results showed that three alkaloid compounds with skeleton piperazine and piperidine, namely (compounds 2 (−4.99 Kcal/mol), 3 (−4.60 Kcal/mol), and 4 (−4.57 Kcal/mol)) had a binding affinity similar to Minoxidil, with also has alkaloid skeleton piperidine−pyrimidine (−4.83 Kcal/mol). The dynamic behavior showed the stability of all androgen receptor compounds with good RMSD, SMSF, and SASA values after being studied with 100 ns molecular dynamics (MD) simulations. This study produced a common thread in discovering a class of alkaloid compounds as inhibitors of androgen receptors that cause alopecia.

Synthesis, In Silico and In Vivo Toxicity Assessment of Functionalized Pyridophenanthridinones via Sequential MW-Assisted Intramolecular Friedel-Crafts Alkylation and Direct C-H Arylation

A rapid, efficient, and original synthesis of novel pyrido[3,2,1-de]phenanthridin-6-ones is reported. First, the key cinnamamide intermediates 8a-f were easily prepared from commercial substituted anilines, cinnamic acid, and 2-bromobenzylbromide in a tandem amidation and N-alkylation protocol. Then, these N-aryl-N-(2-bromobenzyl) cinnamamides 8a-f were subjected to a TFA-mediated intramolecular Friedel-Crafts alkylation followed by a Pd-catalyzed direct C-H arylation to obtain a series of potentially bioactive 4-phenyl-4,5-dihydro-6H,8H-pyrido[3,2,1-de]phenanthridin-6-one derivatives 4a-f in good yields. Finally, the toxicological profile of the prepared final compounds, including their corresponding intermediates, was explored through in silico computational methods, while the acute toxicity toward zebrafish embryos (96 hpf-LC₅₀, 50% lethal concentration) was also determined in the present study.

Anticholinesterase Inhibition, Drug-Likeness Assessment, and Molecular Docking Evaluation of Milk Protein-Derived Opioid Peptides for the Control of Alzheimer’s Disease

The drug-likeness and pharmacokinetic properties of 23 dairy-protein-derived opioid peptides were studied using SwissADME and ADMETlab in silico tools. All the opioid peptides had poor drug-like properties based on violations of Lipinski’s rule-of-five. Moreover, prediction of their pharmacokinetic properties showed that the peptides had poor intestinal absorption and bioavailability. Following this, two well-known opioid peptides (βb-casomorphin-5, βb-casomorphin-7) from A1 bovine milk and caffeine (positive control) were selected for in silico molecular docking and in vitro inhibition study with two cholinesterase enzyme receptors important for the pathogenesis of Alzheimer’s disease. Both peptides showed higher binding free energies and inhibitory activities to butyrylcholinesterase (BChE) than caffeine, but in vitro binding energy values were lower than those from the docking model. Moreover, the two casomorphins had lower inhibitory properties against acetylcholinesterase (AChE) than caffeine, although the docking model predicted the opposite. At 1 mg/mL concentrations, βb-casomorphin-5 and βb-casomorphin-7 showed promising results in inhibiting both cholinesterases (i.e., respectively 34% and 43% inhibition of AChE, and 67% and 81% inhibition of BChE). These dairy-derived opioid peptides have the potential to treat Alzheimer’s disease via cholinesterase inhibition. However, appropriate derivatization may be required to improve their poor predicted intestinal absorption and bioavailability.

Investigation of antidiabetic properties of shikonin by targeting aldose reductase enzyme: In silico and in vitro studies

Diabetes is a complicated multifactorial disorder in which the patient generally observes polyphagia, polydipsia, and polyuria due to uncontrolled growth in blood sugar levels. For its management, the pharmaceutical industry is working day and night to find a better drug with no or least toxicity. That's why nowadays a more focused branch is to use herbal phytoconstituents for its prevention. Shikonin is a naphthoquinone natural dye that is isolated from the plants of the Boraginaceae family and has proven its role as an anti-cancer, anti-inflammatory, and anti-gonadotrophic agent. In our previous study, we have published its anti-diabetic action by inhibiting the enzyme protein tyrosine phosphatase 1B. In this study, we were more focused on finding out the role of Shikonin and its pharmacophores by inhibiting the action of aldose reductase (AR) enzyme. The study was conducted using pharmacophore modeling, molecular docking, and molecular dynamics simulation studies. The absorption, distribution, metabolism, excretion (ADME), and toxicity profile were also evaluated in this study. Along with all the computational biology parameters we also focused on the in vitro activity and kinetic study of inhibitory activity of Shikonin against aldose reductase.

Analysis of micropollutants in a marine outfall using network analysis and decision tree

The presence of micropollutants (MPs), including pharmaceutical, industrial, and pesticidal compounds, threatens both human health and the aquatic ecosystem. The development and extensive use of new chemicals have also inevitably led to the accumulation of MPs in aquatic environments. Recreational beaches are especially vulnerable to contamination, affecting humans and aquatic animals via the absorption of MPs in water during marine activities (e.g., swimming, sailing, and windsurfing). Additionally, marine outfalls in an urbanized coastal city can cause serious chemical and microbial pollution on recreational beaches, leading to an increase in adverse effects on public health and the ecological system. Therefore, the aim of this study was to, with the use of network and decision tree analyses, identify the features and factors that influence the change in MP concentrations in a marine outfall. These analyses were conducted to inspect the relationship between each MP and its hierarchical structure as well as hydrometeorological variables. Additionally, a risk analysis was conducted in this study in which the MPs were prioritized based on their optimized risk quotient values. During our monitoring of MP concentrations over time at the marine outfall, high concentrations of pharmaceutical and industrial compounds were detected when the tide level was low after rainfall. Furthermore, results of the risk analysis and the prioritization revealed that a total of 18 substances identified in our study posed a risk to the ecosystem; these include major ecotoxicologically hazardous substances such as telmisartan, mevinphos, and methiocarb. Results of the network analysis demonstrated distinct trends for pharmaceutical and industrial substances, whilst those for pesticide compounds were irregular. Additionally, the hierarchical structures for most MPs consisted of rainfall, tide level, and antecedent dry hours; this implies that these factors influence MP dynamics. These findings will be helpful for establishing chemical contamination management plans for recreational beaches in the future.

Best practices in current models mimicking drug permeability in the gastrointestinal tract - an UNGAP review

The absorption of orally administered drug products is a complex, dynamic process, dependent on a range of biopharmaceutical properties; notably the aqueous solubility of a molecule, stability within the gastrointestinal tract (GIT) and permeability. From a regulatory perspective, the concept of high intestinal permeability is intrinsically linked to the fraction of the oral dose absorbed. The relationship between permeability and the extent of absorption means that experimental models of permeability have regularly been used as a surrogate measure to estimate the fraction absorbed. Accurate assessment of a molecule's intestinal permeability is of critical importance during the pharmaceutical development process of oral drug products, and the current review provides a critique of in vivo, in vitro and ex vivo approaches. The usefulness of in silico models to predict drug permeability is also discussed and an overview of solvent systems used in permeability assessments is provided. Studies of drug absorption in humans are an indirect indicator of intestinal permeability, but in vitro and ex vivo tools provide initial screening approaches are important tools for direct assessment of permeability in drug development. Continued refinement of the accuracy of in silico approaches and their validation with human in vivo data will facilitate more efficient characterisation of permeability earlier in the drug development process and will provide useful inputs for integrated, end-to-end absorption modelling.

Biochemical and in silico study of leaf and bark extracts from Aphanamixis polystachya against common pathogenic bacteria

Aphanamixis polystachya may be a natural, renewable resource against antibiotic-resistant bacterial infections. The antibacterial activity of A. polystachya leaf and bark extracts was investigated against three antibiotic-resistant bacterial species and one fungus. Methanolic leaf extract showed only limited antibacterial activity but both methanolic and aqueous bark extract showed high antimicrobial activity. In an antioxidant activity test, leaf and bark extracts exhibited 50% free radical scavenging at a concentration of 107.14 ± 3.14 μg/mL and 97.13 ± 3.05 μg/mL, respectively, indicating that bark extracts offer more antioxidative activity than leaf extracts. Bark extracts also showed lower toxicity than leaf extracts. This suggests that bark extracts may offer greater development potential than leaf extracts. The molecular dynamics were also investigated through the simulated exploration of multiple potential interactions to understand the interaction dynamics (root-mean-square deviation, solvent-accessible surface area, radius of gyration, and the hydrogen bonding of chosen compounds to protein targets) and possible mechanisms of inhibition. This molecular modeling of compounds derived from A. polystachya revealed that inhibition may occur by binding to the active sites of the target proteins of the tested bacterial strains. A. polystachya bark extract may be used as a natural source of drugs to control antibiotic-resistant bacteria.

Part-II- in silico drug design: application and success

In silico tools have indeed reframed the steps involved in traditional drug discovery and development process and the term in silico has become a familiar term in pharmaceutical sector like the terms in vitro and in vivo. The successful design of HIV protease inhibitors, Saquinavir, Indinavir and other important medicinal agents, initiated interest of researchers in structure based drug design approaches (SBDD). The interactions between biomolecules and a ligand, binding energy, free energy and stability of biomolecule-ligand complex can be envisioned and predicted by applying molecular docking studies. Protein-ligand, protein-protein, DNA-ligand interactions etc. aid in elucidating molecular level mechanisms of drug molecules. In the Ligand based drug design (LBDD) approaches, QSAR studies have tremendously contributed to the development of antimicrobial, anticancer, antimalarial agents. In the recent years, multiQSAR (mt-QSAR) approaches have been successfully employed for designing drugs against multifactorial diseases. Output of a research in several instances is rewarding when both SBDD and LBDD approaches are combined. Application of in silico studies for prediction of pharmacokinetics was once a real challenge but one can see unlimited number publications comprising tools, data bases which can accurately predict almost all the pharmacokinetic parameters. Absorption, distribution, metabolism, transporters, blood brain barrier permeability, hERG toxicity, P-gp affinity and several toxicological end points can be accurately predicted for a candidate molecule before its synthesis. In silico approaches are greatly encouraged a result of growing limitations and new legislations related to the animal use for research. The combined use of in vitro data and in silico tools will definitely decrease the use of animal testing in the future.In this chapter, in silico approaches and their applications are reviewed and discussed giving suitable examples.

Dietary stigmastane-type saponins as promising dual-target directed inhibitors of SARS-CoV-2 proteases: a structure-based screening

Despite the development of COVID-19 vaccines, at present, there is still no approved antiviral drug against the pandemic. The SARS-CoV-2 3-chymotrypsin-like proteases (S-3CLpro) and papain-like protease (S-PLpro) are essential for the viral proliferation cycle, hence attractive drug targets. Plant-based dietary components that have been extensively reported for antiviral activities may serve as cheap sources of preventive nutraceuticals and/or antiviral drugs. A custom-made library of 176 phytochemicals from five West African antiviral culinary herbs was screened for potential dual-target-directed inhibitors of S-3CLpro and S-PLpro in silico. The docking analysis revealed fifteen steroidal saponins (FSS) from Vernonia amygdalina with the highest binding tendency for the active sites of S-3CLpro and S-PLpro. In an optimized docking analysis, the FSS were further docked against four equilibrated conformers of the S-3CLpro and S-PLpro. Three stigmastane-type steroidal saponins (vernonioside A2, vernonioside A4 and vernonioside D2) were revealed as the lead compounds. These compounds interacted with the catalytic residues of both S-3CLpro and S-PLpro, thereby exhibiting dual inhibitory potential against these SARS-CoV-2 cysteine proteases. The binding free energy calculations further corroborated the static and optimized docking analysis. The complexed proteases with these promising phytochemicals were stable during a full atomistic MD simulation while the phytochemicals exhibited favourable physicochemical and ADMET properties, hence, recommended as promising inhibitors of SARS-CoV-2 cysteine proteases.

FP-ADMET: a compendium of fingerprint-based ADMET prediction models

MOTIVATION

The absorption, distribution, metabolism, excretion, and toxicity (ADMET) of drugs plays a key role in determining which among the potential candidates are to be prioritized. In silico approaches based on machine learning methods are becoming increasing popular, but are nonetheless limited by the availability of data. With a view to making both data and models available to the scientific community, we have developed FPADMET which is a repository of molecular fingerprint-based predictive models for ADMET properties. In this article, we have examined the efficacy of fingerprint-based machine learning models for a large number of ADMET-related properties. The predictive ability of a set of 20 different binary fingerprints (based on substructure keys, atom pairs, local path environments, as well as custom fingerprints such as all-shortest paths) for over 50 ADMET and ADMET-related endpoints have been evaluated as part of the study. We find that for a majority of the properties, fingerprint-based random forest models yield comparable or better performance compared with traditional 2D/3D molecular descriptors.

AVAILABILITY

The models are made available as part of open access software that can be downloaded from https://gitlab.com/vishsoft/fpadmet .

Identification of influencing factors of A. catenella bloom using machine learning and numerical simulation

Alexandrium catenella (A. catenella) is a notorious algal species known to cause paralytic shellfish poisoning (PSP) in Korean coastal waters. There have been numerous studies on its temporal and spatial blooms in Korea. However, its bloom dynamics have not been fully understood because of the complexity in physical, chemical, and biological environments. This study aims to identify the factors that influence A. catenella blooms by applying a numerical model and machine learning. Intensive monitoring of A. catenella was conducted to investigate temporal variations in its population and its spatial distribution in the area with frequent occurrences of PSP bloom initiation. Moreover, a numerical model was built to analyze the ocean physical factors related to the bloom of A. catenella. Based on the information obtained from the monitored and simulated results, the decision tree (DT) method was applied to identify factors that caused the bloom. The outbreak of A. catenella was observed in the eastern coastal water of Geoje Island in 2017, recording a peak density of 4 × 10⁴ (cell L^-1). Retention time and particle scattering demonstrated that the physical force in 2017 was weaker than that in 2018, as shown by the smaller effects of advection and dispersion in 2017. The decision tree model showed that (1) water temperature below 17.21 °C was ideal for the growth of A. catenella, (2) phosphate influenced the growth of the species, and (3) cell density was accelerated with increasing retention time. The results from DT can contribute to the prediction of A. catenella blooms by determining the conditions that cause bloom initiation. Further, they can be used as a practical approach for mitigating HABs. Thus, machine learning and numerical simulation in this study can be a potential approach for effectively managing the bloom of A. catenella.

Novel α-amylase and α-glucosidase inhibitors from selected Nigerian antidiabetic plants: an in silico approach

This study aimed to identify novel α-amylase and α-glucosidase inhibitors from Nigerian antidiabetic plants through in silico approach. Virtual screening of the 93 phytoconstituents was performed, and their inhibitory potentials were ranked based on their docking scores. Five hit molecules were selected for each enzyme target with their hydrogen bonding, hydrophobic, electrostatic, and pi interactions analyzed with discovery studio visualizer. The drug-likeness and ADMET studies of the hit molecules were performed to ascertain their druggability properties. Further, three top-ranked hit molecules were subjected to molecular dynamics simulations. The virtual screening, drug-likeness property, and ADMET studies, and molecular dynamics simulations carried out reveal Newbouldiaquinone A, Foetidin, Chamuvaritin, Cajaflavanone, and Azadirolic acid as potential inhibitors of α-amylase while Chamuvaritin, Newbouldiaquinone A, Flowerone, Scoparic acid A and Nimonol were potential inhibitors of α-glucosidase enzyme.Communicated by Ramaswamy H. Sarma.

Artificial Intelligence in Drug Discovery: A Comprehensive Review of Data-driven and Machine Learning Approaches

As expenditure on drug development increases exponentially, the overall drug discovery process requires a sustainable revolution. Since artificial intelligence (AI) is leading the fourth industrial revolution, AI can be considered as a viable solution for unstable drug research and development. Generally, AI is applied to fields with sufficient data such as computer vision and natural language processing, but there are many efforts to revolutionize the existing drug discovery process by applying AI. This review provides a comprehensive, organized summary of the recent research trends in AI-guided drug discovery process including target identification, hit identification, ADMET prediction, lead optimization, and drug repositioning. The main data sources in each field are also summarized in this review. In addition, an in-depth analysis of the remaining challenges and limitations will be provided, and proposals for promising future directions in each of the aforementioned areas.

A computational approach for designing novel SARS-CoV-2 Mpro inhibitors: combined QSAR, molecular docking, and molecular dynamics simulation techniques

The promising compound T₂₁ for treating COVID-19 at the active site of SARS-CoV-2 M^pro.

QSPR model for Caco-2 cell permeability prediction using a combination of HQPSO and dual-RBF neural network

The Caco-2 cell model is widely used to evaluate the in vitro human intestinal permeability of drugs due to its morphological and functional similarity to human enterocytes. Although it is safe and relatively economic, it is time-consuming. A rapid and accurate quantitative structure-property relationship (QSPR) model of Caco-2 permeability is helpful to improve the efficiency of oral drug development. The aim of our study is to explore the predictive ability of the QSPR model, to study its permeation mechanism, and to develop a potential permeability prediction model, for Caco-2 cells. In our study, a relatively large data set was collected and the abnormal data were eliminated using the Monte Carlo regression and hybrid quantum particle swarm optimization (HQPSO) algorithm. Then, the remaining 1827 compounds were used to establish QSPR models. To generate multiple chemically diverse training and test sets, we used a combination of principal component analysis (PCA) and self-organizing mapping (SOM) neural networks to split the modeling data set characterized by PaDEL-descriptors. After preliminary selection of descriptors by the mean decrease impurity (MDI) method, the HQPSO algorithm was used to select the key descriptors. Six different methods, namely, multivariate linear regression (MLR), support vector machine regression (SVR), xgboost, radial basis function (RBF) neural networks, dual-SVR and dual-RBF were employed to develop QSPR models. The best dual-RBF model was obtained finally with R 2 = 0.91, and R cv5 2 = 0.77, for the training set, and R T 2 = 0.77, for the test set. A series of validation methods were used to assess the robustness and predictive ability of the dual-RBF model under OECD principles. A new application domain (AD) definition method based on the descriptor importance-weighted and distance-based (IWD) method was proposed, and the outliers were analyzed carefully. Combined with the importance of the descriptors used in the dual-RBF model, we concluded that the "H E-state" and hydrogen bonds are important factors affecting the permeability of drugs passing through the Caco-2 cell. Compared with the reported studies, our method exhibits certain advantages in data size, transparency of modeling process and prediction accuracy to some extent, and is a promising tool for virtual screening in the early stage of drug development.

The oxygenated products of cryptotanshinone by biotransformation with Cunninghamella elegans exerting anti-neuroinflammatory effects by inhibiting TLR 4-mediated MAPK signaling pathway

Cryptotanshinone (1), a major bioactive constituent in the traditional Chinese medicinal herb Dan-Shen Salvia miltiorrhiza Bunge, has been reported to possess remarkable pharmacological activities. To improve its bioactivities and physicochemical properties, in the present study, cryptotanshinone (1) was biotransformed with the fungus Cunninghamella elegans AS3.2028. Three oxygenated products (2-4) at C-3 of cryptotanshinone (1) were obtained, among them 2 was a new compound. Their structures were elucidated by comprehensive spectroscopic analysis including HRESIMS, NMR and ECD data. All of the biotransformation products (2-4) were found to inhibit significantly lipopolysaccharide-induced nitric oxide production in BV2 microglia cells with the IC₅₀ values of 0.16-1.16 μM, approximately 2-20 folds stronger than the substrate (1). These biotransformation products also displayed remarkably improved inhibitory effects on the production of inflammatory cytokines (IL-1β, IL-6, TNF-α, COX-2 and iNOS) in BV-2 cells via targeting TLR4 compared to substrate (1). The underlying mechanism of 2 was elucidated by comparative transcriptome analysis, which suggested that it reduced neuroinflammatory mainly through mitogen-activated protein kinase (MAPK) signaling pathway. Western blotting results revealed that 2 downregulated LPS-induced phosphorylation of JNK, ERK, and p38 in MAPK signaling pathway. These findings provide a basal material for the discovery of candidates in treating Alzheimer's disease.

Characteristics of Biopeptides Released In Silico from Collagens Using Quantitative Parameters

The potential of collagens to release biopeptides was evaluated using the BIOPEP-UWM-implemented quantitative criteria including the frequency of the release of fragments with a given activity by selected enzyme(s) (AE), relative frequency of release of fragments with a given activity by selected enzyme(s) (W), and the theoretical degree of hydrolysis (DHt). Cow, pig, sheep, chicken, duck, horse, salmon, rainbow trout, goat, rabbit, and turkey collagens were theoretically hydrolyzed using: stem bromelain, ficin, papain, pepsin, trypsin, chymotrypsin, pepsin+trypsin, and pepsin+trypsin+chymotrypsin. Peptides released from the collagens having comparable AE and W were estimated for their likelihood to be bioactive using PeptideRanker Score. The collagens tested were the best sources of angiotensin I-converting enzyme (ACE) and dipeptidyl peptidase IV (DPP-IV) inhibitors. AE and W values revealed that pepsin and/or trypsin were effective producers of such peptides from the majority of the collagens examined. Then, the SwissTargetPrediction program was used to estimate the possible interactions of such peptides with enzymes and proteins, whereas ADMETlab was applied to evaluate their safety and drug-likeness properties. Target prediction revealed that the collagen-derived peptides might interact with several human proteins, especially proteinases, but with relatively low probability. In turn, their bioactivity may be limited by their short half-life in the body.

In Silico Prediction of Intestinal Permeability by Hierarchical Support Vector Regression

The vast majority of marketed drugs are orally administrated. As such, drug absorption is one of the important drug metabolism and pharmacokinetics parameters that should be assessed in the process of drug discovery and development. A nonlinear quantitative structure-activity relationship (QSAR) model was constructed in this investigation using the novel machine learning-based hierarchical support vector regression (HSVR) scheme to render the extremely complicated relationships between descriptors and intestinal permeability that can take place through various passive diffusion and carrier-mediated active transport routes. The predictions by HSVR were found to be in good agreement with the observed values for the molecules in the training set (n = 53, r² = 0.93, q CV 2 = 0.84, RMSE = 0.17, s = 0.08), test set (n = 13, q² = 0.75-0.89, RMSE = 0.26, s = 0.14), and even outlier set (n = 8, q² = 0.78-0.92, RMSE = 0.19, s = 0.09). The built HSVR model consistently met the most stringent criteria when subjected to various statistical assessments. A mock test also assured the predictivity of HSVR. Consequently, this HSVR model can be adopted to facilitate drug discovery and development.

ATC-NLSP: Prediction of the Classes of Anatomical Therapeutic Chemicals Using a Network-Based Label Space Partition Method

Anatomical Therapeutic Chemical (ATC) classification system proposed by the World Health Organization is a widely accepted drug classification scheme in both academic and industrial realm. It is a multilabeling system which categorizes drugs into multiple classes according to their therapeutic, pharmacological, and chemical attributes. In this study, we adopted a data-driven network-based label space partition (NLSP) method for prediction of ATC classes of a given compound within the multilabel learning framework. The proposed method ATC-NLSP is trained on the similarity-based features such as chemical–chemical interaction and structural and fingerprint similarities of a compound to other compounds belonging to the different ATC categories. The NLSP method trains predictors for each label cluster (possibly intersecting) detected by community detection algorithms and takes the ensemble labels for a compound as final prediction. Experimental evaluation based on the jackknife test on the benchmark dataset demonstrated that our method has boosted the absolute true rate, which is the most stringent evaluation metrics in this study, from 0.6330 to 0.7497, in comparison to the state-of-the-art approaches. Moreover, the community structures of the label relation graph were detected through the label propagation method. The advantage of multilabel learning over the single-label models was shown by label-wise analysis. Our study indicated that the proposed method ATC-NLSP, which adopts ideas from network research community and captures the correlation of labels in a data driven manner, is the top-performing model in the ATC prediction task. We believed that the power of NLSP remains to be unleashed for the multilabel learning tasks in drug discovery. The source codes are freely available at https://github.com/dqwei-lab/ATC.

Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery

Artificial intelligence (AI), and, in particular, deep learning as a subcategory of AI, provides opportunities for the discovery and development of innovative drugs. Various machine learning approaches have recently (re)emerged, some of which may be considered instances of domain-specific AI which have been successfully employed for drug discovery and design. This review provides a comprehensive portrayal of these machine learning techniques and of their applications in medicinal chemistry. After introducing the basic principles, alongside some application notes, of the various machine learning algorithms, the current state-of-the art of AI-assisted pharmaceutical discovery is discussed, including applications in structure- and ligand-based virtual screening, de novo drug design, physicochemical and pharmacokinetic property prediction, drug repurposing, and related aspects. Finally, several challenges and limitations of the current methods are summarized, with a view to potential future directions for AI-assisted drug discovery and design.

Screening of terpenoids as potential therapeutics against Zaire ebolavirus infection through pharmacophore-based drug design

Backgroud: Ebola virus disease (EVD) has spread to various countries in the world and has caused many deaths. Five different virus species can cause EVD, but the most virulent is Zaire ebolavirus (EBOV). The genome of EBOV includes seven genes that encode proteins playing essential roles in the virus lifecycle. Among these proteins, VP24 plays a vital role in the inhibition of the host cells’ immune system. Therefore, VP24 is a potential target for EVD therapy. In the present study, a potential inhibitor of EBOV VP24 activity was identified through pharmacophore-based drug design. Methods: This research was a in silico study, using pharmacophore based molecular docking simulation to obtain inhibitor candidates. Result: Terpenoids were used as VP24 inhibitor candidates. In particular, 55,979 terpenoids were obtained from the PubChem database. An initial screening based on the toxicity prediction test was performed with DataWarrior software: 3,353 ligands were shown to have a favorable toxicity profile, but only 1,375 among them had suitable pharmacophore features. These ligands were used for pharmacophore-based rigid and flexible molecular docking simulations with PDB ID: 4M0Q, chosen as the crystal structure of EBOV VP24. Six ligands predicted to have strong molecular interactions with EBOV VP24 underwent pharmacological property analysis through various software packages, including DataWarrior, SwissADME, admetSAR, pkCSM, and Toxtree. Conclusions: Taxumairol V was identified as the best candidate for EVD drug therapy via EBOV VP24 inhibition based on its molecular properties, predicted molecular interactions with the target molecule, and predicted pharmacological properties.

Identification of a Novel Bcl-2 Inhibitor by Ligand-Based Screening and Investigation of Its Anti-cancer Effect on Human Breast Cancer Cells

Bcl-2 family protein is an important factor in regulating apoptosis and is associated with cancer. The anti-apoptotic proteins of Bcl-2 family, such as Bcl-2, are overexpression in numerous tumors, and contribute to cancer formation, development, and therapy resistance. Therefore, Bcl-2 is a promising target for drug development, and several Bcl-2 inhibitors are currently undergoing clinical trials. In this study, we carried out a QSAR-based virtual screening approach to develop potential Bcl-2 inhibitors from the SPECS database. Surface plasmon resonance (SPR) binding assay was performed to examine the interaction between Bcl-2 protein and the screened inhibitors. After that, we measured the anti-tumor activities of the 8 candidate compounds, and found that compound M1 has significant cytotoxic effect on breast cancer cells. We further proved that compound M1 downregulated Bcl-2 expression and activated apoptosis by inducing mitochondrial dysfunction. In conclusion, we identified a novel Bcl-2 inhibitor by QSAR screening, which exerted significant cytotoxic activity in breast cancer cells through inducing mitochondria-mediated apoptosis.

Three-Level Hepatotoxicity Prediction System Based on Adverse Hepatic Effects

Hepatotoxicity is a major cause of drug withdrawal from the market. To reduce the drug attrition induced by hepatotoxicity, an accurate and efficient hepatotoxicity prediction system must be constructed. In the present study, we constructed a three-level hepatotoxicity prediction system based on different levels of adverse hepatic effects (AHEs) combined with machine learning, using (1) an end point, hepatotoxicity; (2) four hepatotoxicity severity degrees; and (3) specific AHEs. After collecting and curing 15 873 compound-AHE pairs associated with 2017 compounds and 403 AHEs, we constructed 27 models with three end point levels with the random forest algorithm, and obtained accuracies ranging from 67.0 to 78.2% and the area under receiver operating characteristic curves (AUCs) of 0.715-0.875. The 27 models were fully integrated into a tiered hepatotoxicity prediction system. The existence of hepatotoxicity existence, severity degree, and potential AHEs for a given compound could be inferred simultaneously and systematically. Thus, the tiered hepatotoxicity prediction system allows researchers to have significant confidence in confirming compound hepatotoxicity, analyzing hepatotoxicity from multiple perspectives, obtaining warnings for the potential hepatotoxicity severity, and even rapidly selecting the proper in vitro experiments for hepatotoxicity verification. We also applied three external sets (11 drugs or candidates that failed in clinical trials or were withdrawn from the market, the PharmGKB (offsides) database, and an herbal hepatotoxicity data set) to test and validate the prediction ability of our system. Furthermore, the hepatotoxicity prediction system was adapted into a flow framework based on the Konstanz Information Miner, which was made available for researchers.

Computational Prediction of a New ADMET Endpoint for Small Molecules: Anticommensal Effect on Human Gut Microbiota

The human gut microbiota (HGM), which are evolutionarily commensal in the human gastrointestinal system, are crucial to our health. However, HGM can be broadly shaped by multifaceted factors such as intake of drugs. About one-quarter of the existing drugs for humans, which are designed to target human cells rather than HGM, can notably alter the composition of HGM. Therefore, the anticommensal effect of human drugs should be avoided to the maximum extent possible in the drug discovery and development process. Nevertheless, the anticommensal effect of small molecules is a new ADMET (absorption, distribution, metabolism, excretion, and toxicity) end point, which was never predicted with the computational method before. In this work, we present the first machine-learning based consensus classification model with the accuracy (0.811 ± 0.012), precision (0.759 ± 0.032), specificity (0.901 ± 0.019), sensitivity (0.628 ± 0.036), F1-score (0.687 ± 0.023), and AUC (0.814 ± 0.030) respectively on the test set. Furthermore, we develop an easy-to-use "e-Commensal" program for the automatic prediction. Based on this program, virtual-screening of the food-constituent database (FooDB) indicates that 5888 of 23 202 food-relevant compounds are forecasted to possess an anticommensal effect on HGM. Several top-ranked anticommensal compounds in our prediction are further scrutinized and confirmed by experiments in the existing literature. To the best of our knowledge, this is the first classification model and stand-alone software for the prediction of commensal or anticommensal compounds impacting HGM.

ADMETlab: a platform for systematic ADMET evaluation based on a comprehensively collected ADMET database

Current pharmaceutical research and development (R&D) is a high-risk investment which is usually faced with some unexpected even disastrous failures in different stages of drug discovery. One main reason for R&D failures is the efficacy and safety deficiencies which are related largely to absorption, distribution, metabolism and excretion (ADME) properties and various toxicities (T). Therefore, rapid ADMET evaluation is urgently needed to minimize failures in the drug discovery process. Here, we developed a web-based platform called ADMETlab for systematic ADMET evaluation of chemicals based on a comprehensively collected ADMET database consisting of 288,967 entries. Four function modules in the platform enable users to conveniently perform six types of drug-likeness analysis (five rules and one prediction model), 31 ADMET endpoints prediction (basic property: 3, absorption: 6, distribution: 3, metabolism: 10, elimination: 2, toxicity: 7), systematic evaluation and database/similarity searching. We believe that this web platform will hopefully facilitate the drug discovery process by enabling early drug-likeness evaluation, rapid ADMET virtual screening or filtering and prioritization of chemical structures. The ADMETlab web platform is designed based on the Django framework in Python, and is freely accessible at http://admet.scbdd.com/ .

e-Bitter: Bitterant Prediction by the Consensus Voting From the Machine-Learning Methods

In-silico bitterant prediction received the considerable attention due to the expensive and laborious experimental-screening of the bitterant. In this work, we collect the fully experimental dataset containing 707 bitterants and 592 non-bitterants, which is distinct from the fully or partially hypothetical non-bitterant dataset used in the previous works. Based on this experimental dataset, we harness the consensus votes from the multiple machine-learning methods (e.g., deep learning etc.) combined with the molecular fingerprint to build the bitter/bitterless classification models with five-fold cross-validation, which are further inspected by the Y-randomization test and applicability domain analysis. One of the best consensus models affords the accuracy, precision, specificity, sensitivity, F1-score, and Matthews correlation coefficient (MCC) of 0.929, 0.918, 0.898, 0.954, 0.936, and 0.856 respectively on our test set. For the automatic prediction of bitterant, a graphic program “e-Bitter” is developed for the convenience of users via the simple mouse click. To our best knowledge, it is for the first time to adopt the consensus model for the bitterant prediction and develop the first free stand-alone software for the experimental food scientist.

PyBioMed: a python library for various molecular representations of chemicals, proteins and DNAs and their interactions

Background

With the increasing development of biotechnology and informatics technology, publicly available data in chemistry and biology are undergoing explosive growth. Such wealthy information in these data needs to be extracted and transformed to useful knowledge by various data mining methods. Considering the amazing rate at which data are accumulated in chemistry and biology fields, new tools that process and interpret large and complex interaction data are increasingly important. So far, there are no suitable toolkits that can effectively link the chemical and biological space in view of molecular representation. To further explore these complex data, an integrated toolkit for various molecular representation is urgently needed which could be easily integrated with data mining algorithms to start a full data analysis pipeline.

Results

Herein, the python library PyBioMed is presented, which comprises functionalities for online download for various molecular objects by providing different IDs, the pretreatment of molecular structures, the computation of various molecular descriptors for chemicals, proteins, DNAs and their interactions. PyBioMed is a feature-rich and highly customized python library used for the characterization of various complex chemical and biological molecules and interaction samples. The current version of PyBioMed could calculate 775 chemical descriptors and 19 kinds of chemical fingerprints, 9920 protein descriptors based on protein sequences, more than 6000 DNA descriptors from nucleotide sequences, and interaction descriptors from pairwise samples using three different combining strategies. Several examples and five real-life applications were provided to clearly guide the users how to use PyBioMed as an integral part of data analysis projects. By using PyBioMed, users are able to start a full pipelining from getting molecular data, pretreating molecules, molecular representation to constructing machine learning models conveniently.

Conclusion

PyBioMed provides various user-friendly and highly customized APIs to calculate various features of biological molecules and complex interaction samples conveniently, which aims at building integrated analysis pipelines from data acquisition, data checking, and descriptor calculation to modeling. PyBioMed is freely available at http://projects.scbdd.com/pybiomed.html.