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Special Issue "Artificial Intelligence and Computer Aided Drug Design"

A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Biophysics".

Deadline for manuscript submissions: closed (30 April 2019)

Special Issue Editor

Guest Editor
Prof. Andrea Danani

Istituto Dalle Molle Di Studi Sull'intelligenza Artificiale, Manno, Switzerland
Website | E-Mail
Interests: Virtual screening and CADD; Machine learning in molecular simulation data; Protein conformational changes related to drug interactions; In-silico protein–ligand binding, kinetics and thermodynamics; AI in pharmaceutical and clinical data analysis

Special Issue Information

Dear Colleagues,

Artificial Intelligence (AI) is gaining more and more importance in the pharmaceutical sector, deeply transforming the drug discovery process. There are many potential benefits of applying AI techniques to improve the development of new molecules and the identification of new targets, cutting R&D costs and time.

In drug discovery, AI helps in predicting the efficacy and safety of molecules and gives researchers a much broader chemical pallet for the selection of the best molecules for drug testing and delivery. In this context, drug repurposing is another important topic where AI can have a substantial impact. With the huge amount of clinical and pharmaceutical data available to date, AI algorithms find suitable drugs, which can be repurposed for an alternative use in medicine.

Moreover, with the help of AI, it becomes easier to run clinical tests, diagnose diseases and provide the most effective treatment for a particular disease. As it can interpret test results, AI can also look through various sources including publications to correctly diagnose critical ailments.

In this Special Issue of the International Journal of Molecular Sciences, we would like to discuss new approaches based on AI in the drug discovery process and in the repositioning of old molecules, together with their impact on the pharmaceutical pipeline. The goal is to provide an overview of the sectors where AI might play a crucial role in the pharmaceutical world in the next years.

Prof. Andrea Danani
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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. International Journal of Molecular Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Computer aided drug design
  • Machine learning, deep learning
  • Big data
  • Virtual screening
  • Drug discovery and repurposing
  • Drug-target interaction

Published Papers (1 paper)

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Research

Open AccessCommunication
Predicting Apoptosis Protein Subcellular Locations based on the Protein Overlapping Property Matrix and Tri-Gram Encoding
Int. J. Mol. Sci. 2019, 20(9), 2344; https://doi.org/10.3390/ijms20092344
Received: 1 April 2019 / Revised: 25 April 2019 / Accepted: 8 May 2019 / Published: 11 May 2019
PDF Full-text (620 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
To reveal the working pattern of programmed cell death, knowledge of the subcellular location of apoptosis proteins is essential. Besides the costly and time-consuming method of experimental determination, research into computational locating schemes, focusing mainly on the innovation of representation techniques on protein [...] Read more.
To reveal the working pattern of programmed cell death, knowledge of the subcellular location of apoptosis proteins is essential. Besides the costly and time-consuming method of experimental determination, research into computational locating schemes, focusing mainly on the innovation of representation techniques on protein sequences and the selection of classification algorithms, has become popular in recent decades. In this study, a novel tri-gram encoding model is proposed, which is based on using the protein overlapping property matrix (POPM) for predicting apoptosis protein subcellular location. Next, a 1000-dimensional feature vector is built to represent a protein. Finally, with the help of support vector machine-recursive feature elimination (SVM-RFE), we select the optimal features and put them into a support vector machine (SVM) classifier for predictions. The results of jackknife tests on two benchmark datasets demonstrate that our proposed method can achieve satisfactory prediction performance level with less computing capacity required and could work as a promising tool to predict the subcellular locations of apoptosis proteins. Full article
(This article belongs to the Special Issue Artificial Intelligence and Computer Aided Drug Design)
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Int. J. Mol. Sci. EISSN 1422-0067 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
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