scholarly journals Computational chemistry, data mining, high-throughput synthesis and screening - informatics and integration in drug discovery

2001 ◽  
Vol 23 (6) ◽  
pp. 191-192
Author(s):  
Charles J. Manly
2001 ◽  
Vol 23 (6) ◽  
pp. 191-192 ◽  
Author(s):  
Charles J. Manly

Drug discovery today includes considerable focus of laboratory automation and other resources on both combinatorial chemistry and high-throughput screening, and computational chemistry has been a part of pharmaceutical research for many years. The real benefit of these technologies is beyond the exploitation of each individually. Only recently have significant efforts focused on effectively integrating these and other discovery disciplines to realize their larger potential. This technical note will describe one example of these integration efforts.


2000 ◽  
Vol 22 (6) ◽  
pp. 171-173 ◽  
Author(s):  
Michael Entzeroth ◽  
Béatrice Chapelain ◽  
Jacques Guilbert ◽  
Valérie Hamon

High throughput screening has significantly contributed to advances in drug discovery. The great increase in the number of samples screened has been accompanied by increases in costs and in the data required for the investigated compounds. High throughput profiling addresses the issues of compound selectivity and specificity. It combines conventional screening with data mining technologies to give a full set of data, enabling development candidates to be more fully compared.


2019 ◽  
Author(s):  
Michael Gerckens ◽  
Hani Alsafadi ◽  
Darcy Wagner ◽  
Katharina Heinzelmann ◽  
Kenji Schorpp ◽  
...  

2019 ◽  
Author(s):  
Mohammad Atif Faiz Afzal ◽  
Mojtaba Haghighatlari ◽  
Sai Prasad Ganesh ◽  
Chong Cheng ◽  
Johannes Hachmann

<div>We present a high-throughput computational study to identify novel polyimides (PIs) with exceptional refractive index (RI) values for use as optic or optoelectronic materials. Our study utilizes an RI prediction protocol based on a combination of first-principles and data modeling developed in previous work, which we employ on a large-scale PI candidate library generated with the ChemLG code. We deploy the virtual screening software ChemHTPS to automate the assessment of this extensive pool of PI structures in order to determine the performance potential of each candidate. This rapid and efficient approach yields a number of highly promising leads compounds. Using the data mining and machine learning program package ChemML, we analyze the top candidates with respect to prevalent structural features and feature combinations that distinguish them from less promising ones. In particular, we explore the utility of various strategies that introduce highly polarizable moieties into the PI backbone to increase its RI yield. The derived insights provide a foundation for rational and targeted design that goes beyond traditional trial-and-error searches.</div>


2003 ◽  
Vol 9 (1) ◽  
pp. 49-58
Author(s):  
Margit Asmild ◽  
Nicholas Oswald ◽  
Karen M. Krzywkowski ◽  
Søren Friis ◽  
Rasmus B. Jacobsen ◽  
...  

2021 ◽  
pp. 247255522110232
Author(s):  
Michael D. Scholle ◽  
Doug McLaughlin ◽  
Zachary A. Gurard-Levin

Affinity selection mass spectrometry (ASMS) has emerged as a powerful high-throughput screening tool used in drug discovery to identify novel ligands against therapeutic targets. This report describes the first high-throughput screen using a novel self-assembled monolayer desorption ionization (SAMDI)–ASMS methodology to reveal ligands for the human rhinovirus 3C (HRV3C) protease. The approach combines self-assembled monolayers of alkanethiolates on gold with matrix-assisted laser desorption ionization time-of-flight (MALDI TOF) mass spectrometry (MS), a technique termed SAMDI-ASMS. The primary screen of more than 100,000 compounds in pools of 8 compounds per well was completed in less than 8 h, and informs on the binding potential and selectivity of each compound. Initial hits were confirmed in follow-up SAMDI-ASMS experiments in single-concentration and dose–response curves. The ligands identified by SAMDI-ASMS were further validated using differential scanning fluorimetry (DSF) and in functional protease assays against HRV3C and the related SARS-CoV-2 3CLpro enzyme. SAMDI-ASMS offers key benefits for drug discovery over traditional ASMS approaches, including the high-throughput workflow and readout, minimizing compound misbehavior by using smaller compound pools, and up to a 50-fold reduction in reagent consumption. The flexibility of this novel technology opens avenues for high-throughput ASMS assays of any target, thereby accelerating drug discovery for diverse diseases.


ACS Sensors ◽  
2021 ◽  
Author(s):  
Chandrashekhar U. Murade ◽  
Samata Chaudhuri ◽  
Ibtissem Nabti ◽  
Hala Fahs ◽  
Fatima S. M. Refai ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document