Web Resources for Discovery and Development of New Medicines

Molecular descriptor analysis of approved drugs using unsupervised learning for drug repurposing

Machine learning and data-driven approaches are currently being widely used in drug discovery and development due to their potential advantages in decision-making based on the data leveraged from existing sources. Applying these approaches to drug repurposing (DR) studies can identify new relationships between drug molecules, therapeutic targets and diseases that will eventually help in generating new insights for developing novel therapeutics. In the current study, a dataset of 1671 approved drugs is analyzed using a combined approach involving unsupervised Machine Learning (ML) techniques (Principal Component Analysis (PCA) followed by k-means clustering) and Structure-Activity Relationships (SAR) predictions for DR. PCA is applied on all the two dimensional (2D) molecular descriptors of the dataset and the first five Principal Components (PC) were subsequently used to cluster the drugs into nine well separated clusters using k-means algorithm. We further predicted the biological activities for the drug-dataset using the PASS (Predicted Activities Spectra of Substances) tool. These predicted activity values are analyzed systematically to identify repurposable drugs for various diseases. Clustering patterns obtained from k-means showed that every cluster contains subgroups of structurally similar drugs that may or may not have similar therapeutic indications. We hypothesized that such structurally similar but therapeutically different drugs can be repurposed for the native indications of other drugs of the same cluster based on their high predicted biological activities obtained from PASS analysis. In line with this, we identified 66 drugs from the nine clusters which are structurally similar but have different therapeutic uses and can therefore be repurposed for one or more native indications of other drugs of the same cluster. Some of these drugs not only share a common substructure but also bind to the same target and may have a similar mechanism of action, further supporting our hypothesis. Furthermore, based on the analysis of predicted biological activities, we identified 1423 drugs that can be repurposed for 366 new indications against several diseases. In this study, an integrated approach of unsupervised ML and SAR analysis have been used to identify new indications for approved drugs and the study provides novel insights into clustering patterns generated through descriptor level analysis of approved drugs.

Mycobacterium tuberculosis Cell Wall Permeability Model Generation Using Chemoinformatics and Machine Learning Approaches

The drug-resistant strains of Mycobacterium tuberculosis (M.tb) are evolving at an alarming rate, and this indicates the urgent need for the development of novel antitubercular drugs. However, genetic mutations, complex cell wall system of M.tb, and influx-efflux transporter systems are the major permeability barriers that significantly affect the M.tb drugs activity. Thus, most of the small molecules are ineffective to arrest the M.tb cell growth, even though they are effective at the cellular level. To address the permeability issue, different machine learning models that effectively distinguish permeable and impermeable compounds were developed. The enzyme-based (IC₅₀) and cell-based (minimal inhibitory concentration) data were considered for the classification of M.tb permeable and impermeable compounds. It was assumed that the compounds that have high activity in both enzyme-based and cell-based assays possess the required M.tb cell wall permeability. The XGBoost model was outperformed when compared to the other models generated from different algorithms such as random forest, support vector machine, and naïve Bayes. The XGBoost model was further validated using the validation data set (21 permeable and 19 impermeable compounds). The obtained machine learning models suggested that various descriptors such as molecular weight, atom type, electrotopological state, hydrogen bond donor/acceptor counts, and extended topochemical atoms of molecules are the major determining factors for both M.tb cell permeability and inhibitory activity. Furthermore, potential antimycobacterial drugs were identified using computational drug repurposing. All the approved drugs from DrugBank were collected and screened using the developed permeability model. The screened compounds were given as input in the PASS server for the identification of possible antimycobacterial compounds. The drugs that were retained after two filters were docked to the active site of 10 different potential antimycobacterial drug targets. The results obtained from this study may improve the understanding of M.tb permeability and activity that may aid in the development of novel antimycobacterial drugs.

Hydroperoxy steroids and triterpenoids derived from plant and fungi: Origin, structures and biological activities

Hydroperoxides (R-OOH) represent a small family of natural metabolites that have been isolated from higher plants, fungi, and marine organisms. This paper is devoted to the distribution of hydroperoxides in plants, fungi and terrestrial fungal endophytes and their biological activity. Hydroperoxides of plants demonstrate a wide range of biological activities however, antineoplastic and anti-ulcerative are most characteristic with confidence from 91 to 98 percent. For hydroperoxides from fungi, the dominant are antineoplastic and anti-hypercholesterolemic activities with confidence from 89 to 92 percent. Very interesting activity was found for some triterpenoid hydroperoxides, which is characterized as a treatment for the symptoms of dementia. The norlupane hydroperoxide shows activity for the treatment of dementia. It is interesting that the reliability of this activity was very high 97.2%. According to our preliminary data, the norlupane hydroperoxide is apparently the first natural metabolite that showed almost 100 percent activity for the treatment of dementia. However, to confirm these data requires practical and clinical experimental work.

Hydroperoxides derived from marine sources: origin and biological activities

Hydroperoxides are a small and interesting group of biologically active natural marine compounds. All these metabolites contain a group (R-O-O-H). In this mini-review, studies of more than 80 hydroperoxides isolated from bacteria, fungi, algae, and marine invertebrates are described. Hydroperoxides from the red, brown, and green algae exhibit high antineoplastic, anti-inflammatory, and antiprotozoal activity with a confidence of 73 to 94%. Hydroperoxides produced by soft corals showed antineoplastic and antiprotozoal activity with confidence from 81 to 92%. Metabolites derived from sea sponges, mollusks, and other invertebrates showed antineoplastic and antiprotozoal (Plasmodium) activity with confidence from 80 to 90%.

Oxetane-containing metabolites: origin, structures, and biological activities

Cyclobutanes containing one oxygen atom in a molecule are called oxetane-containing compounds (OCC). More than 600 different OCC are found in nature; they are produced by microorganisms, and also found in marine invertebrates and algae. The greatest number of them is found in plants belonging to the genus Taxus. Oxetanes are high-energy oxygen-containing non-aromatic heterocycles that are of great interest as new potential pharmacophores with a significant spectrum of biological activities. The biological activity of OCC that is produced by bacteria and Actinomycetes demonstrates antineoplastic, antiviral (arbovirus), and antifungal activity with confidence an angiogenesis stimulator, respiratory analeptic, and antiallergic activity dominate with confidence from 81 to 99%.

Highly oxygenated isoprenoid lipids derived from fungi and fungal endophytes: Origin and biological activities

This mini review is devoted to highly oxygenated isoprenoid lipids (HOIL) that are produced by fungi and fungal endophytes from various ecological niches, both terrestrial and aquatic. Steroids were distributed as from edible cultivated fungi, as well as fungi collected in forests. Fungal endophytes were generally isolated from plants and cultured to obtain sufficient biomass. Marine fungi were obtained from marine brown and red algae and marine invertebrates such as sponges, corals, worms, crustacea or from marine sediments. HOIL isolated from the terrestrial ecosystem have the pharmacological potential on anti-hypercholesterolemic, anti-neoplastic, anti-eczematic and anti-inflammatory activity estimated with a confidence of 84-90%. HOIL that produced by marine fungal species are predicted as having anti-inflammatory and anti-hypercholesterolemic activity with a confidence of 82-91%. In addition, they may have potential acetylcholinesterase and cell adhesion molecule inhibitors estimated with a confidence of 86-88%.

Peroxy steroids derived from plant and fungi and their biological activities

Peroxides represent a large and interesting group of biologically active natural compounds. All these metabolites contain a peroxide group (R-O-O-R). This review describes studies of more than 60 peroxides isolated from plants and fungi. Most of the plant peroxy steroids exhibit high antiprotozoal (Plasmodium) activity with a confidence of up to 95%, while steroids harvested from fungi show more antineoplastic activity with a confidence of up to 94%. In addition, more than 20 different activities of both groups of peroxides with a probability of 78 to 90% have also been predicted using computer program PASS.