Tool For Lead Optimization Research Articles

Microwave synthesized ionic liquids as green catalysts for the synthesis of benzimidazoles: Spectral and computational studies for potential anticancer activity

• Innovative, green chemical processes for solvent free, quicker, efficient reaction times & products i.e., MW & ILs. • Modifying conventional organic transformations with green catalysts (ILs) e. g. Benzimidazoles. • Identifying the possibility to develop benzimidazole derivatives as promising anti-breast cancer drugs. • As breast cancer mutant, oxidoreductase enzyme (3hb5) was used as a molecular docking target in this investigation. • Further, Ligand's and/or protein's surface proved to be a useful tool for lead optimization. Imidazolium based ionic liquid (IL) [bmim] Br or 1-butyl-3-methyl imidazolium bromide synthesized from 1-methyl imidazole and butyl bromide by microwave (MW) method and is used for efficient synthesis of benzimidazoles from o-phenylene diamine and aldehydes at room temperature (RT). Our attention has been drawn to the development of cost-effective and environmentally friendly catalytic systems for chemical synthesis because of the increasing need for greener and more sustainable technologies. This procedure has various benefits, including excellent yields (≈100%) of synthesized benzimidazole derivatives in 3 h and product recovery with minimal experimental work-up. The catalytic activity of ionic liquid can be recovered and reused up to four times without losing its activity. The 17 beta-hydroxysteroid dehydrogenase (PDB ID: 3hb5) was used as a target in a molecular docking study of these benzimidazole derivatives using Flare V5GUI software. They have the greatest LF dG score, ranging from −8.03 to −6.3 kcal/mol, indicating that these benzimidazole derivatives could be developed as promising anti-breast cancer drug candidates. Further, the electrostatic complementarity approach outlined here is a useful and versatile technique for drug development. The ability to observe EC values over the surface of a ligand or protein has a huge advantage when considered optimization.

Interpretable-ADMET: a web service for ADMET prediction and optimization based on deep neural representation.

In the process of discovery and optimization of lead compounds, it is difficult for non-expert pharmacologists to intuitively determine the contribution of substructure to a particular property of a molecule. In this work, we develop a user-friendly web service, named interpretable-absorption, distribution, metabolism, excretion and toxicity (ADMET), which predict 59 ADMET-associated properties using 90 qualitative classification models and 28 quantitative regression models based on graph convolutional neural network and graph attention network algorithms. In interpretable-ADMET, there are 250729 entries associated with 59 kinds of ADMET-associated properties for 80167 chemical compounds. In addition to making predictions, interpretable-ADMET provides interpretation models based on gradient-weighted class activation map for identifying the substructure, which is important to the particular property. Interpretable-ADMET also provides an optimize module to automatically generate a set of novel virtual candidates based on matched molecular pair rules. We believe that interpretable-ADMET could serve as a useful tool for lead optimization in drug discovery. Interpretable-ADMET is available at http://cadd.pharmacy.nankai.edu.cn/interpretableadmet/. Supplementary data are available at Bioinformatics online.

Protein-Ligand Binding Affinity Predictions by Implicit Solvent Simulations: A Tool for Lead Optimization?

Continuum electrostatics is combined with rigorous free-energy calculations in an effort to deliver a reliable and efficient method for in silico lead optimization. The methodology is tested by calculation of the relative binding free energies of a set of inhibitors of neuraminidase, cyclooxygenase2, and cyclin-dependent kinase 2. The calculated free energies are compared to the results obtained with explicit solvent simulations and empirical scoring functions. For cyclooxygenase2, deficiencies in the continuum electrostatics theory are identified and corrected with a modified simulation protocol. For neuraminidase, it is shown that a continuum representation of the solvent leads to markedly different protein-ligand interactions compared to the explicit solvent simulations, and a reconciliation of the two protocols is problematic. Cyclin-dependent kinase 2 proves more challenging, and none of the methods employed in this study yield high quality predictions. Despite the differences observed, for these systems, the use of an implicit solvent framework to predict the ranking of congeneric inhibitors to a protein is shown to be faster, as accurate or more accurate than the explicit solvent protocol, and superior to empirical scoring schemes.