Artificial Intelligence and Machine Learning in Medicinal Chemistry and Validation of Emerging Drug Targets

Abstract

Artificial learning and machine learning is playing a pivotal role is the society especially in the field of medicinal chemistry and drug discovery. Particularly its algorithms, neural networks or other recurrent networks drive this area. In this review, we have taken into account the diverse use of AI in a number of pharmaceutical industries including discovery of drugs, repurposing, development of pharmaceutical drug and its clinical trials. In addition, the efficiency of these artificial or machine learning programs in achieving the target drugs in short time period, along with accurate dosage and cost effectively of the drug has also been discussed. Numerous applications of AI in property prediction such as ADMET have been used for prediction of strength of this technology in QSAR. In case of de-novo synthesis, it results in generation of novel drug molecules with unique design proving this a promising field fir drug design. Moreover, its involvement in synthetic planning, ease of synthesis and much more contribute to automated drug discovery in near future.

Telomerase Gene Therapy: A Remission Towards Cancer

“…Silencing or inhibition of TERT or TERC activity are further applications within the realm of gene therapy that exploit constitutive activation of TERT in cancer [73][74][75].For example, siRNA or antisense RNA targeting TERT has been a popular gene therapy [76][77][78][79][80][81], with a small number of studies examining various strategies (such as expression of the c-terminal TERT polypeptide (hTERTC27) [61,82,83], CRISPR/Cas9 targeting an oncogenic TERT promoter mutant allele that allows constitutive activation in cancer [84], or microRNA-mediated downregulation of TERT by targeting the 30 -UTR. These methods, in general, promote cellular senescence, cause apoptotic tumor cell death, and slow tumor growth.…”

“…Phase: III Endogenous processing of the vaccine yields an HLA class I peptide that elicits both CD4+ and CD8+ responses, resulting in significant cytotoxic T-lymphocyte activation [74].…”

The Influence of AI: The Revolutionary Effects of Artificial Intelligence in Healthcare Sector

“…Analyzing large genomic datasets enables these methods to identify complex patterns that are frequently difficult to find through manual analysis. An innovative study utilized a sophisticated neural network to detect genetic variations linked to autism spectrum disorder (ASD), accurately forecasting the presence of ASD based exclusively on genomic information [74]. Transcriptomic profiling can be used to categories tumors into clinically relevant molecular subtypes in the field of oncology.…”

TNFR2 Antagonist and Agonist: A Potential Therapeutics in Cancer Immunotherapy

“…Another anti-TNFR2 mAb (clone TR75-89), which is likewise a TNFR2 agonist but does not compete with TNF-α, was studied. Because of cross-linking by FcγR-expressing cells, monoclonal antibodies targeting TNFRSF members can now be used as agonists in pre-clinical trials[90][91][92]. TR75-54.7 and TR75-89 were shown to bind to recombinant mouse FcγRII and FcγRIII, but no interaction with FcγRI or FcγRIV was identified, indicating that these mAbs can be cross-linked by a subset of mice FcγRs but should not be anticipated to promote antibody-dependent cellular phagocytosis (ADCP).TNFR2 mAbs decreased the development of CT26 tumors in immunocompetent mouse models when contrasted to control animals that undergone saline or hamster IgG control mAbs.…”