Sustainable production of fine chemicals and fuels from renewable energy resources is an important process in the current scenario of the energy crisis. In this study, we have reported complete hydrogenation of eugenol, which is an important biomass-derived molecule using ruthenium-containing nickel hydrotalcite (NiRu-HT)-type materials and isopropyl alcohol as a solvent. A series of group VIII metals containing bimetallic Ni-HT-type materials were synthesized, characterized, and demonstrated to participate in eugenol conversion, and among which, Ni-Ru-HT was found to give the highest selectivity toward alkyl cyclohexanol. The reaction conditions, including reaction temperature, hydrogen pressure, and the Ni/Ru ratio, were optimized. Based on the results, it can be concluded that eugenol was first hydrogenated to 4-propyl guaiacol and then underwent simultaneous demethoxylation and aromatic ring hydrogenation to form 4-propylcyclohexanol. Moreover, various lignin-derived phenolic compounds can be efficiently converted into alkyl cyclohexanols and aromatic compounds into alkyl cyclohexane. The in situ formation of metallic ruthenium on the surface of NiRu-HT evident from TPR and TEM analyses is responsible for observed high yields of ring hydrogenated products of eugenol (i.e., 4-propylcyclohexanol).

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: The advancement of computing and technology has invaded all the dimensions of science. Artificial intelligence (AI) is one core branch of Computer Science, which has percolated to all the arenas of science and technology, from core engineering to medicines. Thus, AI has found its way for application in the field of medicinal chemistry and heath care. The conventional methods of drug design have been replaced by computer-aided designs of drugs in recent times. AI is being used extensively to improve the design techniques and required time of the drugs. Additionally, the target proteins can be conveniently identified using AI, which enhances the success rate of the designed drug. The AI technology is used in each step of the drug designing procedure, which decreases the health hazards related to preclinical trials and also reduces the cost substantially. The AI is an effective tool for data mining based on the huge pharmacological data and machine learning process. Hence, AI has been used in de novo drug design, activity scoring, virtual screening and in silico evaluation in the properties (absorption, distribution, metabolism, excretion and toxicity) of a drug molecule. Various pharmaceutical companies have teamed up with AI companies for faster progress in the field of drug development, along with the healthcare system. The review covers various aspects of AI (Machine learning, Deep learning, Artificial neural networks) in drug design. It also provides a brief overview of the recent progress by the pharmaceutical companies in drug discovery by associating with different AI companies.

Abstract:: Motivated by evidence garnered from literature probing the use of triazoles in drug discovery and development we reported the utilization of bioisosteric replacement and molecular hybridization in this review. Bio-isosteric replacement has played a significant role in modulating rapid and versatile strategy in synthesizing molecules with multifaceted medicinal properties. Molecular hybridization seeks to conjugate two molecular fragments with diverse applications under very mild reaction conditions. In this regard, 1, 2, 3-triazole is a well-known scaffold with widespread occurrence in medicinal compounds. It is characterized to have several bioactivities such as anti-microbial, anti-cancer, anti-viral, analgesic, anti-inflammatory effects. Furthermore, the structural features of 1,2,3-triazoles enable it to mimic different functional groups justifying its use as bio-isostere for the synthesis of new molecules of medicinal interest which we have reported briefly.

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