3 Biotech最新文献

A few decades ago, drug discovery and development were limited to a bunch of medicinal chemists working in a lab with enormous amount of testing, validations, and synthetic procedures, all contributing to considerable investments in time and wealth to get one drug out into the clinics. The advancements in computational techniques combined with a boom in multi-omics data led to the development of various bioinformatics/pharmacoinformatics/cheminformatics tools that have helped speed up the drug development process. But with the advent of artificial intelligence (AI), machine learning (ML) and deep learning (DL), the conventional drug discovery process has been further rationalized. Extensive biological data in the form of big data present in various databases across the globe acts as the raw materials for the ML/DL-based approaches and helps in accurate identifications of patterns and models which can be used to identify therapeutically active molecules with much fewer investments on time, workforce and wealth. In this review, we have begun by introducing the general concepts in the drug discovery pipeline, followed by an outline of the fields in the drug discovery process where ML/DL can be utilized. We have also introduced ML and DL along with their applications, various learning methods, and training models used to develop the ML/DL-based algorithms. Furthermore, we have summarized various DL-based tools existing in the public domain with their application in the drug discovery paradigm which includes DL tools for identification of drug targets and drug-target interaction such as DeepCPI, DeepDTA, WideDTA, PADME DeepAffinity, and DeepPocket. Additionally, we have discussed various DL-based models used in protein structure prediction, de novo design of new chemical scaffolds, virtual screening of chemical libraries for hit identification, absorption, distribution, metabolism, excretion, and toxicity (ADMET) prediction, metabolite prediction, clinical trial design, and oral bioavailability prediction. In the end, we have tried to shed light on some of the successful ML/DL-based models used in the drug discovery and development pipeline while also discussing the current challenges and prospects of the application of DL tools in drug discovery and development. We believe that this review will be useful for medicinal and computational chemists searching for DL tools for use in their drug discovery projects.

The present study illustrates the transformation ability of two wild-type bacterial strains of Rhizobium rhizogenes (MTCC 532 and MTCC 2364) on the embryogenic callus and callus-derived plantlets of a threatened Indian orchid, Dendrobium ovatum. Co-culture of the bacterium with the explants gave marginal hairy root phenotype that failed to multiply in the culture medium. Some primary and secondary metabolites were subdued in infected explants. Moscatilin, the stilbenoid active principle in D. ovatum, was found below the detection limit. The presence of two metabolites viz., Laudanosine, a benzyltetrahydroisoquinoline alkaloid and Lyciumin B, a cyclic peptide, were detected exclusively in the infected explants. The subjugated amino acids and phenolics in the infected plantlets were routed to produce phytoanticipins, and phenanthrenes, strengthening the defence mechanism in infected tissues. This research implies that the plant's defence mechanism activation could have prevented the extensive hairy root formation in the explants, even though nodulations and phenotype transitions were witnessed. Moscatilin has a structural resemblance with Resveratrol, a phytoalexin that combats bacterial and fungal pathogens. The study favours the possibility of Moscatlin being a precursor for phenanthrene compounds, thereby serving as a 'phytoanticipin' during the infection phase.

Supplementary information: The online version contains supplementary material available at 10.1007/s13205-022-03180-9.