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A special issue of Molecules (ISSN 1420-3049). This special issue belongs to the section "Chemical Biology".

Deadline for manuscript submissions: closed (30 June 2019) | Viewed by 17840

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Dr. J. B. Brown

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Guest Editor

Life Science Informatics Research Unit, Laboratory for Molecular Biosciences, Kyoto University Graduate School of Medicine, Kyoto, Sakyo, Japan
Interests: computational chemical biology; computational drug discovery; clinical informatics; translational life science informatics

Prof. Dr. Jürgen Bajorath

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Guest Editor

Department of Life Science Informatics, B‐IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich‐Wilhelms‐Universität, Bonn, Germany
Interests: chemoinformatics; medicinal chemistry; chemical biology; drug design; drug discovery
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Special Issue Information

Dear Colleagues,

The biological screening of compound collections continues to provide many new active chemical entities for further consideration. Large-magnitude screening campaigns typically require computational support for data analysis and the selection of preferred hits for follow-up studies. Here, the requirements for lead-like molecules in medicinal chemistry and probe-like compounds in chemical biology differ. Furthermore, the experimental studies must often be further extended through computational means, for example to identify analogues of interesting active compounds or map currently known targets. To these ends, computational chemical biology is tasked with delivering interpretation and prediction tools with significant potential to complement experimental investigations. In this Special Issue, we are seeking contributions focusing on new computational methodologies, practical solutions, and perspectives with immediate relevance for chemical biology. Papers on diverse sub-topics such as, for example, molecular structure–selectivity analysis, single- and multi-target assay data exploration, or bioactivity modelling through artificial intelligence approaches including, but not limited to, machine learning are welcome.

Manuscripts are ideally written to include both experimental and computational viewpoints. In addition, papers reporting new computational concepts for chemical biology are highly desired.

Assoc. Prof. Dr. J. B. Brown
Prof. Dr. Jürgen Bajorath
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. Molecules 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 2700 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

Bioactivity assay analysis
Computational structure–activity relationship
High-throughput screening
Pattern recognition/machine learning

Published Papers (5 papers)

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Research

17 pages, 2693 KiB

Open AccessArticle

Applicability Domain of Active Learning in Chemical Probe Identification: Convergence in Learning from Non-Specific Compounds and Decision Rule Clarification

by Ahsan Habib Polash, Takumi Nakano, Shunichi Takeda and J.B. Brown

Molecules 2019, 24(15), 2716; https://doi.org/10.3390/molecules24152716 - 26 Jul 2019

Cited by 7 | Viewed by 3058

Abstract

Efficient identification of chemical probes for the manipulation and understanding of biological systems demands specificity for target proteins. Computational means to optimize candidate compound selection for experimental selectivity evaluation are being sought. The active learning virtual screening method has demonstrated the ability to efficiently converge on predictive models with reduced datasets, though its applicability domain to probe identification has yet to be determined. In this article, we challenge active learning’s ability to predict inhibitory bioactivity profiles of selective compounds when learning from chemogenomic features found in non-selective ligand-target pairs. Comparison of controls versus multiple molecule representations de-convolutes factors contributing to predictive capability. Experiments using the matrix metalloproteinase family demonstrate maximum probe bioactivity prediction achieved from only approximately 20% of non-probe bioactivity; this data volume is consistent with prior chemogenomic active learning studies despite the increased difficulty from chemical biology experimental settings used here. Feature weight analyses are combined with a custom visualization to unambiguously detail how active learning arrives at classification decisions, yielding clarified expectations for chemogenomic modeling. The results influence tactical decisions for computational probe design and discovery. Full article

(This article belongs to the Special Issue Computational Chemical Biology)

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25 pages, 6120 KiB

Open AccessArticle

High Impact: The Role of Promiscuous Binding Sites in Polypharmacology

by Natacha Cerisier, Michel Petitjean, Leslie Regad, Quentin Bayard, Manon Réau, Anne Badel and Anne-Claude Camproux

Molecules 2019, 24(14), 2529; https://doi.org/10.3390/molecules24142529 - 10 Jul 2019

Cited by 9 | Viewed by 4381

Abstract

The literature focuses on drug promiscuity, which is a drug’s ability to bind to several targets, because it plays an essential role in polypharmacology. However, little work has been completed regarding binding site promiscuity, even though its properties are now recognized among the key factors that impact drug promiscuity. Here, we quantified and characterized the promiscuity of druggable binding sites from protein-ligand complexes in the high quality Mother Of All Databases while using statistical methods. Most of the sites (80%) exhibited promiscuity, irrespective of the protein class. Nearly half were highly promiscuous and able to interact with various types of ligands. The corresponding pockets were rather large and hydrophobic, with high sulfur atom and aliphatic residue frequencies, but few side chain atoms. Consequently, their interacting ligands can be large, rigid, and weakly hydrophilic. The selective sites that interacted with one ligand type presented less favorable pocket properties for establishing ligand contacts. Thus, their ligands were highly adaptable, small, and hydrophilic. In the dataset, the promiscuity of the site rather than the drug mainly explains the multiple interactions between the drug and target, as most ligand types are dedicated to one site. This underlines the essential contribution of binding site promiscuity to drug promiscuity between different protein classes. Full article

(This article belongs to the Special Issue Computational Chemical Biology)

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17 pages, 2207 KiB

Open AccessArticle

Error Tolerance of Machine Learning Algorithms across Contemporary Biological Targets

by Thomas M. Kaiser and Pieter B. Burger

Molecules 2019, 24(11), 2115; https://doi.org/10.3390/molecules24112115 - 04 Jun 2019

Cited by 10 | Viewed by 3329

Abstract

Machine learning continues to make strident advances in the prediction of desired properties concerning drug development. Problematically, the efficacy of machine learning in these arenas is reliant upon highly accurate and abundant data. These two limitations, high accuracy and abundance, are often taken together; however, insight into the dataset accuracy limitation of contemporary machine learning algorithms may yield insight into whether non-bench experimental sources of data may be used to generate useful machine learning models where there is a paucity of experimental data. We took highly accurate data across six kinase types, one GPCR, one polymerase, a human protease, and HIV protease, and intentionally introduced error at varying population proportions in the datasets for each target. With the generated error in the data, we explored how the retrospective accuracy of a Naïve Bayes Network, a Random Forest Model, and a Probabilistic Neural Network model decayed as a function of error. Additionally, we explored the ability of a training dataset with an error profile resembling that produced by the Free Energy Perturbation method (FEP+) to generate machine learning models with useful retrospective capabilities. The categorical error tolerance was quite high for a Naïve Bayes Network algorithm averaging 39% error in the training set required to lose predictivity on the test set. Additionally, a Random Forest tolerated a significant degree of categorical error introduced into the training set with an average error of 29% required to lose predictivity. However, we found the Probabilistic Neural Network algorithm did not tolerate as much categorical error requiring an average of 20% error to lose predictivity. Finally, we found that a Naïve Bayes Network and a Random Forest could both use datasets with an error profile resembling that of FEP+. This work demonstrates that computational methods of known error distribution like FEP+ may be useful in generating machine learning models not based on extensive and expensive in vitro-generated datasets. Full article

(This article belongs to the Special Issue Computational Chemical Biology)

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21 pages, 2211 KiB

Open AccessArticle

An In Silico Approach for Assessment of the Membrane Transporter Activities of Phenols: A Case Study Based on Computational Models of Transport Activity for the Transporter Bilitranslocase

by Katja Venko and Marjana Novič

Molecules 2019, 24(5), 837; https://doi.org/10.3390/molecules24050837 - 27 Feb 2019

Cited by 2 | Viewed by 2898

Abstract

Phenols are the most abundant naturally accessible antioxidants present in a human normal diet. Since numerous beneficial applications of phenols as preventive agents in various diseases were revealed, the evaluation of phenols bioavailability is of high interest of researchers, consumers and drug manufacturers. The hydrophilic nature of phenols makes a cell membrane penetration difficult, which imply an alternative way of uptake via membrane transporters. However, the structural and functional data of membrane transporters are limited, thus the in silico modelling is really challenging and urgent tool in elucidation of transporter ligands. Focus of this research was a particular transporter bilitranslocase (BTL). BTL has a broad tissue expression (vascular endothelium, absorptive and excretory epithelia) and can transport wide variety of poly-aromatic compounds. With available BTL data (pKi [mmol/L] for 120 organic compounds) a robust and reliable QSAR models for BTL transport activity were developed and extrapolated on 300 phenolic compounds. For all compounds the transporter profiles were assessed and results show that dietary phenols and some drug candidates are likely to interact with BTL. Moreover, synopsis of predictions from BTL models and hits/predictions of 20 transporters from Metrabase and Chembench platforms were revealed. With such joint transporter analyses a new insights for elucidation of BTL functional role were acquired. Regarding limitation of models for virtual profiling of transporter interactions the computational approach reported in this study could be applied for further development of reliable in silico models for any transporter, if in vitro experimental data are available. Full article

(This article belongs to the Special Issue Computational Chemical Biology)

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12 pages, 3225 KiB

Open AccessFeature PaperArticle

Data-Driven Exploration of Selectivity and Off-Target Activities of Designated Chemical Probes

by Filip Miljković and Jürgen Bajorath

Molecules 2018, 23(10), 2434; https://doi.org/10.3390/molecules23102434 - 23 Sep 2018

Cited by 9 | Viewed by 3504

Abstract

Chemical probes are of central relevance for chemical biology. To unambiguously explore the role of target proteins in triggering or mediating biological functions, small molecules used as probes should ideally be target-specific; at least, they should have sufficiently high selectivity for a primary target. We present a thorough analysis of currently available activity data for designated chemical probes to address several key questions: How well defined are chemical probes? What is their level of selectivity? Is there evidence for additional activities? Are some probes “better” than others? Therefore, highly curated chemical probes were collected and their selectivity was analyzed on the basis of publicly available compound activity data. Different selectivity patterns were observed, which distinguished designated high-quality probes. Full article

(This article belongs to the Special Issue Computational Chemical Biology)

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