Computer-Aided Drug Design

A special issue of Computation (ISSN 2079-3197).

Deadline for manuscript submissions: closed (15 September 2019) | Viewed by 7478

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Guest Editor
Faculty of Pharmacy, University of Ljubljana, Kongresni trg 12, 1000 Ljubljana, Slovenia
Interests: drug design; medicinal chemistry; molecular modelling; antibacterial agents; anticancer agents
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Special Issue Information

Dear Colleagues,

Computer-aided drug design (CADD) is a state-of-the-art method used in academic institutions and pharmaceutical companies to develop new drugs. CADD is used in all steps of the drug discovery process, from hit identification, hit expansion, to hit-to-lead and lead optimization. It enables an efficient design–synthesis–biological evaluation optimization cycle to improve on-target activity, to eliminate off-target activities and to improve physico-chemical and ADMET properties.

In this Special Issue of Computation we invite researchers to submit full research papers, communications and review articles reporting recent advances in computer-aided drug design, successful case studies reporting the identification and optimization of new bioactive compounds using CADD, and studies describing CADD applications to understand the interaction of small molecules and relevant macromolecular targets.

Dr. Tihomir Tomašič
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Computation is an international peer-reviewed open access monthly journal published by MDPI.

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Keywords

  • molecular modelling
  • docking
  • 3D-pharmacophore modelling
  • virtual screening
  • molecular dynamics simulations
  • chemoinformatics
  • QSAR

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Published Papers (1 paper)

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Research

30 pages, 8121 KiB  
Article
Exploring the Chemical Space of Cytochrome P450 Inhibitors Using Integrated Physicochemical Parameters, Drug Efficiency Metrics and Decision Tree Models
by Yusra Sajid Kiani and Ishrat Jabeen
Computation 2019, 7(2), 26; https://doi.org/10.3390/computation7020026 - 24 May 2019
Cited by 5 | Viewed by 6138
Abstract
The cytochrome P450s (CYPs) play a central role in the metabolism of various endogenous and exogenous compounds including drugs. CYPs are vulnerable to inhibition and induction which can lead to adverse drug reactions. Therefore, insights into the underlying mechanism of CYP450 inhibition and [...] Read more.
The cytochrome P450s (CYPs) play a central role in the metabolism of various endogenous and exogenous compounds including drugs. CYPs are vulnerable to inhibition and induction which can lead to adverse drug reactions. Therefore, insights into the underlying mechanism of CYP450 inhibition and the estimation of overall CYP inhibitor properties might serve as valuable tools during the early phases of drug discovery. Herein, we present a large data set of inhibitors against five major metabolic CYPs (CYP1A2, CYP2C9, CYP2C19, CYP2D6 and CYP3A4) for the evaluation of important physicochemical properties and ligand efficiency metrics to define property trends across various activity levels (active, efficient and inactive). Decision tree models for CYP inhibition were developed with an accuracy >90% for both the training set and 10-folds cross validation. Overall, molecular weight (MW), hydrogen bond acceptors/donors (HBA/HBD) and lipophilicity (clogP/logPo/w) represent important physicochemical descriptors for CYP450 inhibitors. However, highly efficient CYP inhibitors show mean MW, HBA, HBD and logP values between 294.18–482.40,5.0–8.2,1–7.29 and 1.68–2.57, respectively. Our results might help in optimization of toxicological profiles associated with new chemical entities (NCEs), through a better understanding of inhibitor properties leading to CYP-mediated interactions. Full article
(This article belongs to the Special Issue Computer-Aided Drug Design)
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