Special Issue "In Silico Drug Design and Discovery: Big Data for Small Molecule Design"

A special issue of Biomolecules (ISSN 2218-273X). This special issue belongs to the section "Bioinformatics and Systems Biology".

Deadline for manuscript submissions: 31 May 2022 | Viewed by 2194

Special Issue Editors

Prof. Antonio Lavecchia
E-Mail Website
Guest Editor
Department of Pharmacy, “Drug Discovery Lab”, University of Naples “Federico II”, Via D. Montesano 49, 80131 Naples, Italy
Interests: drug discovery; medicinal chemistry; molecular modeling; polypharmacology; artificial intelligence; machine learning
Dr. Carmen Cerchia
E-Mail Website
Guest Editor
Department of Pharmacy, University of Naples “Federico II”, via D. Montesano, 49, 80131 Napoli, Italy
Interests: computer aided drug design; drug discovery; medicinal chemistry; structure based drug design; molecular modeling; polypharmacology; data mining

Special Issue Information

Dear Colleagues,

Life sciences heavily rely on data collected in different ways, for example, through experimental work, medical observations, or computer simulations, to name a few. Advances in novel technologies, such as high-throughput screening and readout, next-generation sequencing, and “-omics” approaches, represent the main drivers of the exponentially increasing amount of data being generated at a fast pace, part of which is available in public databases (e.g., ChEMBL, PubChem, PDB).

Taking advantage of this wealth of information is critical to improve decision making in drug discovery projects. For instance, structure–activity relationships (SARs) can be extracted on a large scale and used to complement chemical optimization efforts. 

Therefore, there is a growing interest in computational approaches to exploit this amount of data and their complexity, including data mining and visualization techniques, predictive models, and machine learning algorithms. 

In this context, this Special Issue has been conceptualized to showcase recent progresses and current trends in the use of in silico approaches leveraging big data and extracting useful knowledge to support all aspects of drug design and discovery. Topics of interest include but are not limited to data mining, molecular modeling, compound bioactivity prediction, and machine learning. Experimental and theoretical research studies are welcome; multidisciplinary approaches are particularly encouraged.

We look forward to your contributions.

Prof. Antonio Lavecchia
Dr. Carmen Cerchia
Guest Editors

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 submissions that pass pre-check are 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. Biomolecules is an international peer-reviewed open access monthly 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 2100 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

  • drug discovery
  • molecular modeling
  • medicinal chemistry
  • chemoinformatics
  • data mining
  • machine learning

Published Papers (2 papers)

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Research

Article
Feature Reduction for Molecular Similarity Searching Based on Autoencoder Deep Learning
Biomolecules 2022, 12(4), 508; https://doi.org/10.3390/biom12040508 - 27 Mar 2022
Viewed by 463
Abstract
The concept of molecular similarity has been commonly used in rational drug design, where structurally similar molecules are examined in molecular databases to retrieve functionally similar molecules. The most used conventional similarity methods used two-dimensional (2D) fingerprints to evaluate the similarity of molecules [...] Read more.
The concept of molecular similarity has been commonly used in rational drug design, where structurally similar molecules are examined in molecular databases to retrieve functionally similar molecules. The most used conventional similarity methods used two-dimensional (2D) fingerprints to evaluate the similarity of molecules towards a target query. However, these descriptors include redundant and irrelevant features that might impact the performance of similarity searching methods. Thus, this study proposed a new approach for identifying the important features of molecules in chemical datasets based on the representation of the molecular features using Autoencoder (AE), with the aim of removing irrelevant and redundant features. The proposed approach experimented using the MDL Data Drug Report standard dataset (MDDR). Based on experimental findings, the proposed approach performed better than several existing benchmark similarity methods such as Tanimoto Similarity Method (TAN), Adapted Similarity Measure of Text Processing (ASMTP), and Quantum-Based Similarity Method (SQB). The results demonstrated that the performance achieved by the proposed approach has proven to be superior, particularly with the use of structurally heterogeneous datasets, where it yielded improved results compared to other previously used methods with the similar goal of improving molecular similarity searching. Full article
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Article
Towards the De Novo Design of HIV-1 Protease Inhibitors Based on Natural Products
Biomolecules 2021, 11(12), 1805; https://doi.org/10.3390/biom11121805 - 01 Dec 2021
Viewed by 951
Abstract
Acquired immunodeficiency syndrome (AIDS) caused by the human immunodeficiency virus (HIV) continues to be a public health problem. In 2020, 680,000 people died from HIV-related causes, and 1.5 million people were infected. Antiretrovirals are a way to control HIV infection but not to [...] Read more.
Acquired immunodeficiency syndrome (AIDS) caused by the human immunodeficiency virus (HIV) continues to be a public health problem. In 2020, 680,000 people died from HIV-related causes, and 1.5 million people were infected. Antiretrovirals are a way to control HIV infection but not to cure AIDS. As such, effective treatment must be developed to control AIDS. Developing a drug is not an easy task, and there is an enormous amount of work and economic resources invested. For this reason, it is highly convenient to employ computer-aided drug design methods, which can help generate and identify novel molecules. Using the de novo design, novel molecules can be developed using fragments as building blocks. In this work, we develop a virtual focused compound library of HIV-1 viral protease inhibitors from natural product fragments. Natural products are characterized by a large diversity of functional groups, many sp3 atoms, and chiral centers. Pseudo-natural products are a combination of natural products fragments that keep the desired structural characteristics from different natural products. An interactive version of chemical space visualization of virtual compounds focused on HIV-1 viral protease inhibitors from natural product fragments is freely available in the supplementary material. Full article
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