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Special Issue "Frontiers in Computational Chemistry for Drug Discovery"

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

Deadline for manuscript submissions: closed (20 June 2018)

Special Issue Editor

Guest Editor
Prof. Dr. F. Javier Luque

Department of Nutrition, Food Sciences and Gastronomy, Faculty of Pharmacy and Food Sciences, Institute of Biomedicine (IBUB) and Institute of Theoretical and Computational Chemistry (IQTC), University of Barcelona, Av. Prat de la Riba 171, E-08921 Santa Coloma de Gramenet, Spain
Website | E-Mail
Interests: computational biology; molecular modeling; molecular simulations; drug design; multitarget compounds; neurodegeneration; antiviral compounds

Special Issue Information

Dear Colleagues,

Nowadays, computational methods pervade almost all aspects of drug discovery. Computer-assisted tools contribute to the decision-making process along the entire drug discovery pipeline, including the validation of suitable targets, high-throughput screening of molecular libraries, optimization of lead compounds, and the balance between pharmacological potency and physico-chemical and pharmacokinetic properties. This tendency will be reinforced in the next few years due to the continued increases in computer power, and the elaboration of sophisticated algorithms to capture the physico-chemical principles that underlie the activity of drugs. This effort should enable drug discovery methodology to evolve from approximate to more rigorous methods. How should computational methods evolve to ameliorate the success of drug discovery? The answer to this question is related to the identification of the current limitations faced by computational algorithms to unveil the delicate balance between factors that determine both potency and ADMET properties of drug candidates. This Special Issue aims to provide a forum for the dissemination of the latest information on new computational approaches and methods in the exciting area of drug discovery.

We welcome original articles and short communications, as well as a limited number of review articles, on new approaches and methods of computational chemistry for drug discovery. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on the website.

Prof. Dr. F. Javier Luque
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. Molecules 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 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

  • drug discovery
  • ligand-receptor recognition
  • binding affinity
  • binding kinetics
  • computational chemistry
  • enhanced sampling techniques
  • quantum mechanics
  • solvation
  • free energy methods
  • structure-based design

Published Papers (10 papers)

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Research

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Open AccessArticle Fingerprint-Based Machine Learning Approach to Identify Potent and Selective 5-HT2BR Ligands
by , , , , and
Molecules 2018, 23(5), 1137; https://doi.org/10.3390/molecules23051137
Received: 16 April 2018 / Revised: 5 May 2018 / Accepted: 7 May 2018 / Published: 10 May 2018
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Abstract
The identification of subtype-selective GPCR (G-protein coupled receptor) ligands is a challenging task. In this study, we developed a computational protocol to find compounds with 5-HT2BR versus 5-HT1BR selectivity. Our approach employs the hierarchical combination of machine learning methods,
[...] Read more.
The identification of subtype-selective GPCR (G-protein coupled receptor) ligands is a challenging task. In this study, we developed a computational protocol to find compounds with 5-HT2BR versus 5-HT1BR selectivity. Our approach employs the hierarchical combination of machine learning methods, docking, and multiple scoring methods. First, we applied machine learning tools to filter a large database of druglike compounds by the new Neighbouring Substructures Fingerprint (NSFP). This two-dimensional fingerprint contains information on the connectivity of the substructural features of a compound. Preselected subsets of the database were then subjected to docking calculations. The main indicators of compounds’ selectivity were their different interactions with the secondary binding pockets of both target proteins, while binding modes within the orthosteric binding pocket were preserved. The combined methodology of ligand-based and structure-based methods was validated prospectively, resulting in the identification of hits with nanomolar affinity and ten-fold to ten thousand-fold selectivities. Full article
(This article belongs to the Special Issue Frontiers in Computational Chemistry for Drug Discovery)
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Open AccessFeature PaperArticle Accurate Estimation of the Standard Binding Free Energy of Netropsin with DNA
by , , , , , and
Molecules 2018, 23(2), 228; https://doi.org/10.3390/molecules23020228
Received: 23 December 2017 / Revised: 14 January 2018 / Accepted: 19 January 2018 / Published: 25 January 2018
Cited by 3 | PDF Full-text (10134 KB) | HTML Full-text | XML Full-text
Abstract
DNA is the target of chemical compounds (drugs, pollutants, photosensitizers, etc.), which bind through non-covalent interactions. Depending on their structure and their chemical properties, DNA binders can associate to the minor or to the major groove of double-stranded DNA. They can also intercalate
[...] Read more.
DNA is the target of chemical compounds (drugs, pollutants, photosensitizers, etc.), which bind through non-covalent interactions. Depending on their structure and their chemical properties, DNA binders can associate to the minor or to the major groove of double-stranded DNA. They can also intercalate between two adjacent base pairs, or even replace one or two base pairs within the DNA double helix. The subsequent biological effects are strongly dependent on the architecture of the binding motif. Discriminating between the different binding patterns is of paramount importance to predict and rationalize the effect of a given compound on DNA. The structural characterization of DNA complexes remains, however, cumbersome at the experimental level. In this contribution, we employed all-atom molecular dynamics simulations to determine the standard binding free energy of DNA with netropsin, a well-characterized antiviral and antimicrobial drug, which associates to the minor groove of double-stranded DNA. To overcome the sampling limitations of classical molecular dynamics simulations, which cannot capture the large change in configurational entropy that accompanies binding, we resort to a series of potentials of mean force calculations involving a set of geometrical restraints acting on collective variables. Full article
(This article belongs to the Special Issue Frontiers in Computational Chemistry for Drug Discovery)
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Open AccessArticle QNA-Based Prediction of Sites of Metabolism
by , , , , and
Molecules 2017, 22(12), 2123; https://doi.org/10.3390/molecules22122123
Received: 9 November 2017 / Revised: 30 November 2017 / Accepted: 28 November 2017 / Published: 1 December 2017
Cited by 1 | PDF Full-text (2698 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Metabolism of xenobiotics (Greek xenos: exogenous substances) plays an essential role in the prediction of biological activity and testing for the subsequent research and development of new drug candidates. Integration of various methods and techniques using different computational and experimental approaches is
[...] Read more.
Metabolism of xenobiotics (Greek xenos: exogenous substances) plays an essential role in the prediction of biological activity and testing for the subsequent research and development of new drug candidates. Integration of various methods and techniques using different computational and experimental approaches is one of the keys to a successful metabolism prediction. While multiple structure-based and ligand-based approaches to metabolism prediction exist, the most important problem arises at the first stage of metabolism prediction: detection of the sites of metabolism (SOMs). In this paper, we describe the application of Quantitative Neighborhoods of Atoms (QNA) descriptors for prediction of the SOMs using potential function method, as well as several different machine learning techniques: naïve Bayes, random forest classifier, multilayer perceptron with back propagation and convolutional neural networks, and deep neural networks. Full article
(This article belongs to the Special Issue Frontiers in Computational Chemistry for Drug Discovery)
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Open AccessFeature PaperArticle Structure-Based Design of Potent and Selective Ligands at the Four Adenosine Receptors
by , , , , , and
Molecules 2017, 22(11), 1945; https://doi.org/10.3390/molecules22111945
Received: 20 October 2017 / Revised: 7 November 2017 / Accepted: 8 November 2017 / Published: 10 November 2017
Cited by 1 | PDF Full-text (4569 KB) | HTML Full-text | XML Full-text
Abstract
The four receptors that signal for adenosine, A1, A2A, A2B and A3 ARs, belong to the superfamily of G protein-coupled receptors (GPCRs). They mediate a number of (patho)physiological functions and have attracted the interest of the biopharmaceutical
[...] Read more.
The four receptors that signal for adenosine, A1, A2A, A2B and A3 ARs, belong to the superfamily of G protein-coupled receptors (GPCRs). They mediate a number of (patho)physiological functions and have attracted the interest of the biopharmaceutical sector for decades as potential drug targets. The many crystal structures of the A2A, and lately the A1 ARs, allow for the use of advanced computational, structure-based ligand design methodologies. Over the last decade, we have assessed the efficient synthesis of novel ligands specifically addressed to each of the four ARs. We herein review and update the results of this program with particular focus on molecular dynamics (MD) and free energy perturbation (FEP) protocols. The first in silico mutagenesis on the A1AR here reported allows understanding the specificity and high affinity of the xanthine-antagonist 8-Cyclopentyl-1,3-dipropylxanthine (DPCPX). On the A2AAR, we demonstrate how FEP simulations can distinguish the conformational selectivity of a recent series of partial agonists. These novel results are complemented with the revision of the first series of enantiospecific antagonists on the A2BAR, and the use of FEP as a tool for bioisosteric design on the A3AR. Full article
(This article belongs to the Special Issue Frontiers in Computational Chemistry for Drug Discovery)
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Open AccessArticle Rapid Screening of Active Components with an Osteoclastic Inhibitory Effect in Herba epimedii Using Quantitative Pattern–Activity Relationships Based on Joint-Action Models
by , , , and
Molecules 2017, 22(10), 1767; https://doi.org/10.3390/molecules22101767
Received: 7 October 2017 / Revised: 14 October 2017 / Accepted: 16 October 2017 / Published: 19 October 2017
PDF Full-text (2630 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Screening of bioactive components is important for modernization and quality control of herbal medicines, while the traditional bioassay-guided phytochemical approach is time-consuming and laborious. The presented study proposes a strategy for rapid screening of active components from herbal medicines. As a case study,
[...] Read more.
Screening of bioactive components is important for modernization and quality control of herbal medicines, while the traditional bioassay-guided phytochemical approach is time-consuming and laborious. The presented study proposes a strategy for rapid screening of active components from herbal medicines. As a case study, the quantitative pattern–activity relationship (QPAR) between compounds and the osteoclastic inhibitory effect of Herba epimedii, a widely used herbal medicine in China, were investigated based on joint models. For model construction, standard mixtures data showed that the joint-action models are better than the partial least-squares (PLS) model. Then, the Good2bad value, which could reflect components’ importance based on Monte Carlo sampling, was coupled with the joint-action models for screening of active components. A compound (baohuoside I) and a component composed of compounds with retention times in the 6.9–7.9 min range were selected by our method. Their inhibition rates were higher than icariin, the key bioactive compound in Herba epimedii, which could inhibit osteoclast differentiation and bone resorption in a previous study. Meanwhile, the half-maximal effective concentration, namely, EC50 value of the selected component was 7.54 μg/mL, much smaller than that of baohuoside I—77 μg/mL—which indicated that there is synergistic action between compounds in the selected component. The results clearly show our proposed method is simple and effective in screening the most-bioactive components and compounds, as well as drug-lead components, from herbal medicines. Full article
(This article belongs to the Special Issue Frontiers in Computational Chemistry for Drug Discovery)
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Open AccessArticle Insights into the Effect of the G245S Single Point Mutation on the Structure of p53 and the Binding of the Protein to DNA
by , , , , and
Molecules 2017, 22(8), 1358; https://doi.org/10.3390/molecules22081358
Received: 2 August 2017 / Revised: 13 August 2017 / Accepted: 13 August 2017 / Published: 16 August 2017
Cited by 3 | PDF Full-text (2800 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
The transcription factor p53 is a potent tumor suppressor dubbed as the “guardian of the genome” because of its ability to orchestrate protective biological outputs in response to a variety of oncogenic stresses. Mutation and thus inactivation of p53 can be found in
[...] Read more.
The transcription factor p53 is a potent tumor suppressor dubbed as the “guardian of the genome” because of its ability to orchestrate protective biological outputs in response to a variety of oncogenic stresses. Mutation and thus inactivation of p53 can be found in 50% of human tumors. The majority are missense mutations located in the DNA binding region. Among them, G245S is known to be a structural hotspot mutation. To understand the behaviors and differences between the wild-type and mutant, both a dimer of the wild type p53 (wt-p53) and its G245S mutant (G245S-mp53), complexed with DNA, were simulated using molecular dynamics for more than 1 μs. wt-p53 and G245S-mp53 apo monomers were simulated for 1 μs as well. Conformational analyses and binding energy evaluations performed underline important differences and therefore provide insights to understand the G245S-mp53 loss of function. Our results indicate that the G245S mutation destabilizes several structural regions in the protein that are crucial for DNA binding when found in its apo form and highlight differences in the mutant-DNA complex structure compared to the wt protein. These findings not only provide means that can be applied to other p53 mutants but also serve as structural basis for further studies aimed at the development of cancer therapies based on restoring the function of p53. Full article
(This article belongs to the Special Issue Frontiers in Computational Chemistry for Drug Discovery)
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Open AccessArticle Metal Atom Effect on the Photophysical Properties of Mg(II), Zn(II), Cd(II), and Pd(II) Tetraphenylporphyrin Complexes Proposed as Possible Drugs in Photodynamic Therapy
by , , , and
Molecules 2017, 22(7), 1093; https://doi.org/10.3390/molecules22071093
Received: 26 May 2017 / Revised: 22 June 2017 / Accepted: 28 June 2017 / Published: 30 June 2017
Cited by 6 | PDF Full-text (1557 KB) | HTML Full-text | XML Full-text
Abstract
The effects of Mg, Zn, Cd, and Pd dications on the photophysical properties of the tetraphenylporphyrin ligand have been explored, considering the corresponding complexes and by using the density functional theory and its time-dependent extension. Results show that absorption wavelengths do not change
[...] Read more.
The effects of Mg, Zn, Cd, and Pd dications on the photophysical properties of the tetraphenylporphyrin ligand have been explored, considering the corresponding complexes and by using the density functional theory and its time-dependent extension. Results show that absorption wavelengths do not change significantly when the metal ion changes contrary to what happens to the singlet–triplet energy gaps (ΔES−T) and the spin-orbit matrix elements ΨSnHsoΨTm. The most probable intersystem spin crossing (ISC) pathways for the population of the lowest triplet states have been explored. Our findings can contribute to rationalize the available experimental data and promote the potential therapeutic use of these compounds as photosensitizers in photodynamic therapy (PDT). Full article
(This article belongs to the Special Issue Frontiers in Computational Chemistry for Drug Discovery)
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Open AccessArticle Exploring the Pivotal Role of the CK2 Hinge Region Sub-Pocket in Binding with Tricyclic Quinolone Analogues by Computational Analysis
by , , , , , and
Molecules 2017, 22(5), 840; https://doi.org/10.3390/molecules22050840
Received: 14 April 2017 / Revised: 12 May 2017 / Accepted: 17 May 2017 / Published: 19 May 2017
PDF Full-text (2453 KB) | HTML Full-text | XML Full-text
Abstract
Protein kinase CK2 has been considered as an attractive therapeutic target of cancer therapy. The tricyclic quinoline compound CX-4945 is the first representative of CK2 inhibitors used in human clinical trials. The binding of non-2,6-naphtyridine substituted compounds 27e (IC50 > 500 nM)
[...] Read more.
Protein kinase CK2 has been considered as an attractive therapeutic target of cancer therapy. The tricyclic quinoline compound CX-4945 is the first representative of CK2 inhibitors used in human clinical trials. The binding of non-2,6-naphtyridine substituted compounds 27e (IC50 > 500 nM) and 27h (IC50 > 1000 nM) to CK2 is abolished. However, the unbinding mechanisms due to the key pharmacophore group replacement of compounds 27e and 27h are unveiled. In the present work, combined computational analysis was performed to investigate the underlying structural basis of the low-affinity of two systems. As indicated in the results, the loss of hydrogen bonds between the non-2,6-naphtyridine and the hinge region destroyed the proper recognition of the two complexes. Besides, the allosteric mechanisms between the deviated ligands and the changed regions (G-loop, C-loop and β4/β5 loop) are proposed. Furthermore, energetic analysis was evaluated by detailed energy calculation and residue-based energy decomposition. More importantly, the summary of known polar pharmacophore groups elucidates the pivotal roles of hinge region sub-pocket in the binding of CK2 inhibitors. These results provide rational clues to the fragment-based design of more potent CK2 inhibitors. Full article
(This article belongs to the Special Issue Frontiers in Computational Chemistry for Drug Discovery)
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Review

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Open AccessReview Evolution of In Silico Strategies for Protein-Protein Interaction Drug Discovery
by , , , , and
Molecules 2018, 23(8), 1963; https://doi.org/10.3390/molecules23081963
Received: 17 July 2018 / Revised: 3 August 2018 / Accepted: 4 August 2018 / Published: 6 August 2018
PDF Full-text (5805 KB) | HTML Full-text | XML Full-text
Abstract
The advent of advanced molecular modeling software, big data analytics, and high-speed processing units has led to the exponential evolution of modern drug discovery and better insights into complex biological processes and disease networks. This has progressively steered current research interests to understanding
[...] Read more.
The advent of advanced molecular modeling software, big data analytics, and high-speed processing units has led to the exponential evolution of modern drug discovery and better insights into complex biological processes and disease networks. This has progressively steered current research interests to understanding protein-protein interaction (PPI) systems that are related to a number of relevant diseases, such as cancer, neurological illnesses, metabolic disorders, etc. However, targeting PPIs are challenging due to their “undruggable” binding interfaces. In this review, we focus on the current obstacles that impede PPI drug discovery, and how recent discoveries and advances in in silico approaches can alleviate these barriers to expedite the search for potential leads, as shown in several exemplary studies. We will also discuss about currently available information on PPI compounds and systems, along with their usefulness in molecular modeling. Finally, we conclude by presenting the limits of in silico application in drug discovery and offer a perspective in the field of computer-aided PPI drug discovery. Full article
(This article belongs to the Special Issue Frontiers in Computational Chemistry for Drug Discovery)
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Open AccessReview Dynamic Docking: A Paradigm Shift in Computational Drug Discovery
by , , , and
Molecules 2017, 22(11), 2029; https://doi.org/10.3390/molecules22112029
Received: 27 October 2017 / Revised: 18 November 2017 / Accepted: 19 November 2017 / Published: 22 November 2017
Cited by 5 | PDF Full-text (1630 KB) | HTML Full-text | XML Full-text
Abstract
Molecular docking is the methodology of choice for studying in silico protein-ligand binding and for prioritizing compounds to discover new lead candidates. Traditional docking simulations suffer from major limitations, mostly related to the static or semi-flexible treatment of ligands and targets. They also
[...] Read more.
Molecular docking is the methodology of choice for studying in silico protein-ligand binding and for prioritizing compounds to discover new lead candidates. Traditional docking simulations suffer from major limitations, mostly related to the static or semi-flexible treatment of ligands and targets. They also neglect solvation and entropic effects, which strongly limits their predictive power. During the last decade, methods based on full atomistic molecular dynamics (MD) have emerged as a valid alternative for simulating macromolecular complexes. In principle, compared to traditional docking, MD allows the full exploration of drug-target recognition and binding from both the mechanistic and energetic points of view (dynamic docking). Binding and unbinding kinetic constants can also be determined. While dynamic docking is still too computationally expensive to be routinely used in fast-paced drug discovery programs, the advent of faster computing architectures and advanced simulation methodologies are changing this scenario. It is feasible that dynamic docking will replace static docking approaches in the near future, leading to a major paradigm shift in in silico drug discovery. Against this background, we review the key achievements that have paved the way for this progress. Full article
(This article belongs to the Special Issue Frontiers in Computational Chemistry for Drug Discovery)
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