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Cheminformatics utilizing machine learning (ML) techniques have opened up a new horizon in drug discovery. This is owing to vast chemical space expansion with rocketing numbers of expected hits and lead compounds that match druggable macromolecular targets, in particular from natural compounds. Due to the natural products' (NP) structural complexity, uniqueness, and diversity, they could occupy a bigger space in pharmaceuticals, allowing the industry to pursue more selective leads in the nanomolar range of binding affinity. ML is an essential part of each step of the drug design pipeline, such as target prediction, compound library preparation, and lead optimization. Notably, molecular mechanic and dynamic simulations, induced docking, and free energy perturbations are essential in predicting best binding poses, binding free energy values, and molecular mechanics force fields. Those applications have leveraged from artificial intelligence (AI), which decreases the computational costs required for such costly simulations. This review aimed to describe chemical space and compound libraries related to NPs. High-throughput screening utilized for fractionating NPs and high-throughput virtual screening and their strategies, and significance, are reviewed. Particular emphasis was given to AI approaches, ML tools, algorithms, and techniques, especially in drug discovery of macrocyclic compounds and approaches in computer-aided and ML-based drug discovery. Anthraquinone derivatives were discussed as a source of new lead compounds that can be developed using ML tools for diverse medicinal uses such as cancer, infectious diseases, and metabolic disorders. Furthermore, the power of principal component analysis in understanding relevant protein conformations, and molecular modeling of protein-ligand interaction were also presented. Apart from being a concise reference for cheminformatics, this review is a useful text to understand the application of ML-based algorithms to molecular dynamics simulation and in silico absorption, distribution, metabolism, excretion, and toxicity prediction.

Human diseases are generally influenced by SNPs (single nucleotide polymorphisms). The mutations in amino acid residues generated by deleterious SNPs contribute to the structural and functional diversity of the encoded protein. Tumor necrosis factor-α (TNF-α), Glucocorticoid receptor gene (NR3C1), and Cytochrome P450 3A5 (CYP3A5) play a key role in glucocorticoid resistance susceptibility in humans. Possible causative mutations could be used as therapeutic targets and diagnostic markers for glucocorticoid resistance. This study evaluated the missense SNPs of TNF-α, NR3C1, and CYP3A5 to predict their impact on amino acid changes, protein interaction, and functional stability. The protein sequence of dbSNP was obtained and used online in silico method to screen deleterious mutants for the in silico analysis. In the coding regions of TNF-α, NR3C1, and CYP3A5, 14 deleterious mutations were discovered. The protein functional and stability changes in the amino acid between native and mutant energy were identified by analyzing the changes in the hydrogen bonding of these mutants from native, which were all measured using Swiss PDB and PyMOL. F446S and R439K had the highest root-mean-square deviation (RMSD) values among the 14 deleterious mutants. Additionally, the conserved region of amino acid protein interaction was analyzed. This study could aid in the discovery of new detrimental mutations in TNF-α, NR3C1, and CYP3A5, as well as the development of long-term therapy for corticosteroid resistance in several inflammatory diseases. However, more research into the deleterious mutations of the TNF-α, NR3C1, and CYP3A5 genes is needed to determine their role in corticosteroid resistance.

Data regarding treatment outcomes among childhood TB patients are lacking in Malaysia. The present study aimed to evaluate the treatment outcomes and predictors of unsuccessful treatment outcomes among childhood TB patients in four TB high-burden states of Malaysia. This was a retrospective cohort study conducted at 13 healthcare centers in four states of Malaysia, namely, Sabah, Sarawak, Selangor, and Penang. During the study period, a total of 8932 TB patients were enrolled for treatment at the study sites, of whom 206 (2.31%) were children. The majority of the childhood TB patients were female (52.9%) and belonged to the age group of 6–10 years (42.7%). Pulmonary TB accounted for 70.9% of childhood TB. Among childhood PTB patients, 50% were sputum smear negative. One hundred and seventy-eight patients (86.4%) were successfully treated (87 were cured and 91 completed treatment). Among 28 (13.6%) patients with unsuccessful treatment outcomes, 13 (6.3%) died, 3 (1.5%) failed treatment, 9 (4.4%) defaulted, and 3 (1.5%) were transferred out. Multivariate analysis revealed that patients’ age (5–14 years) (OR = 0.279, p-value = 0.006) and male gender (OR = 0.390, p-value = 0.046) had a statistically significant negative association with unsuccessful treatment outcomes. The prevalence of childhood TB in the current study was comparable to the recently published national estimates. The study sites reached the WHO target of treatment success. Special attention to patients with identified risk factors can improve treatment outcomes.

Stigmasterol is an unsaturated phytosterol belonging to the class of tetracyclic triterpenes. It is one of the most common plant sterols, found in a variety of natural sources, including vegetable fats or oils from many plants. Currently, stigmasterol has been examined via in vitro and in vivo assays and molecular docking for its various biological activities on different metabolic disorders. The findings indicate potent pharmacological effects such as anticancer, anti-osteoarthritis, anti-inflammatory, anti-diabetic, immunomodulatory, antiparasitic, antifungal, antibacterial, antioxidant, and neuroprotective properties. Indeed, stigmasterol from plants and algae is a promising molecule in the development of drugs for cancer therapy by triggering intracellular signaling pathways in numerous cancers. It acts on the Akt/mTOR and JAK/STAT pathways in ovarian and gastric cancers. In addition, stigmasterol markedly disrupted angiogenesis in human cholangiocarcinoma by tumor necrosis factor-α (TNF-α) and vascular endothelial growth factor receptor-2 (VEGFR-2) signaling down-regulation. The association of stigmasterol and sorafenib promoted caspase-3 activity and down-regulated levels of the anti-apoptotic protein Bcl-2 in breast cancer. Antioxidant activities ensuring lipid peroxidation and DNA damage lowering conferred to stigmasterol chemoprotective activities in skin cancer. Reactive oxygen species (ROS) regulation also contributes to the neuroprotective effects of stigmasterol, as well as dopamine depletion and acetylcholinesterase inhibition. The anti-inflammatory properties of phytosterols involve the production of anti-inflammatory cytokines, the decrease in inflammatory mediator release, and the inhibition of inducible nitric oxide synthase (iNOS) and cyclooxygenase-2 (COX-2). Stigmasterol exerts anti-diabetic effects by reducing fasting glucose, serum insulin levels, and oral glucose tolerance. Other findings showed the antiparasitic activities of this molecule against certain strains of parasites such as Trypanosoma congolense (in vivo) and on promastigotes and amastigotes of the Leishmania major (in vitro). Some stigmasterol-rich plants were able to inhibit Candida albicans, virusei, and tropicalis at low doses. Accordingly, this review outlines key insights into the pharmacological abilities of stigmasterol and the specific mechanisms of action underlying some of these effects. Additionally, further investigation regarding pharmacodynamics, pharmacokinetics, and toxicology is recommended.

Colorectal cancer is one of the most prevalent cancer types. Although there have been breakthroughs in its treatments, a better understanding of the molecular mechanisms and genetic involvement in colorectal cancer will have a substantial role in producing novel and targeted treatments with better safety profiles. In this review, the main molecular pathways and driver genes that are responsible for initiating and propagating the cascade of signaling molecules reaching carcinoma and the aggressive metastatic stages of colorectal cancer were presented. Protein kinases involved in colorectal cancer, as much as other cancers, have seen much focus and committed efforts due to their crucial role in subsidizing, inhibiting, or changing the disease course. Moreover, notable improvements in colorectal cancer treatments with in silico studies and the enhanced selectivity on specific macromolecular targets were discussed. Besides, the selective multi-target agents have been made easier by employing in silico methods in molecular de novo synthesis or target identification and drug repurposing.

The importance of cosmetics in our lives is immeasurable. Covering items from daily personal hygienic products to skincare, it has become essential to consumers that the items that they use are safe and effective. Since natural products are from natural sources, and therefore considered “natural” and “green” in the public’s eyes, the rise in demand for such products is not surprising. Even so, factoring in the need to remain on trend and innovative, cosmetic companies are on a constant search for new ingredients and inventive new formulations. Based on numerous literature, the seed of Swietenia macrophylla has been shown to possess several potential “cosmetic-worthy” bioproperties, such as skin whitening, photoprotective, antioxidant, antimicrobial, etc. These properties are vital in the cosmetic business, as they ultimately contribute to the “ageless” beauty that many consumers yearn for. Therefore, with further refinement and research, these active phytocompounds may be a great contribution to the cosmetic field in the near future.

Isopenicillin N synthase (IPNS) related Non-haem iron-dependent oxygenases and oxidases (NHIDOX) demonstrated a striking structural conservativeness, even with low protein sequence homology. It is evident that these enzymes have an architecturally similar catalytic centre with active ligands lining the reactive pocket. Deacetoxycephalosporin C synthase (DAOCS), isopenicillin N synthase (IPNS), deacetylcephalosporin C synthase (DACS), clavaminate synthase 1 and 2 (CAS1 and 2) are important bacterial enzymes that catalyze the formation of β-lactam antibiotics belonging to this enzyme family. Most plant enzyme members within this subfamily namely flavonol synthase (FLS), leucoanthocyanidin dioxygenase (LDOX), anthocyanidin synthase (ANS), 1-aminocyclopropane-1-carboxylic acid oxidase (ACCO), gibberellin 20-oxidase (G 20 O), desacetoxyvindoline-4-hydroxylase (D4H), flavanone 3β-hydroxylase (F3H), and hyoscyamine 6β-hydroxylase (H6H) are involved in catalyzing the biosyntheses of plant secondary metabolites. With the advancement of protein structural analysis software, it is possible to predict the catalytic sites of protein that shared a structural resemblance. By exploiting the superimposition model of DAOCS-IPNS, DAOCS-IPNS-CAS, G 20 O-LDOX, FLS-LDOX, ACCO-LDOX, D4H-LDOX, F3H-LDOX and H6H-LDOX model; a computational protocol for predicting the catalytic sites of proteins is now made available. This study shows that without the crystallized or nuclear magnetic resonance (NMR) structures of most NHIDOX enzyme, the plausible catalytic sites of protein can be forecasted using this structural bioinformatics approach. Keywords: Enzyme, catalytic sites, isopenicillin N synthase, ligands

Enzymes categorized under the 2OG-Fe (II) oxygenase superfamily and Taurine catabolism dioxygenase TauD family demonstrated a striking structural conserveness even with low protein sequence homology. It is evident that these enzymes have an architecturally similar to catalytic centre with active ligands lining the reactive pocket. Deacetoxycephalosporin C synthase (DAOCS), isopenicillin N synthase (IPNS), deacetylcephalosporin C synthase (DACS), clavaminate synthase 1 and 2 (CAS1 and 2) are important bacterial enzymes that catalyze the formation of β-lactam antibiotics. With the advancement of protein structural analysis software, it is possible to predict the catalytic sites of protein that shared a structural resemblance. By exploiting the superimposition model of DAOCS-IPNS, DAOCS-CAS1 and IPNS-CAS1, a computational protocol for predicting the catalytic sites of proteins is now made available. This study shows that without the crystallized or NMR structures of DACS and CAS2, the plausible catalytic sites of protein can be forecasted using this structural bioinformatics approach. Key words: Isopenicillin N synthase (IPNS), deacetoxycephalosporin C synthase (DAOCS), deacetylcephalosporin C synthase (DACS), clavaminate synthase (CAS), 2OG-Fe (II) oxygenase superfamily.