Special Issue "Machine Learning for Molecular Modelling in Drug Design"

A special issue of Biomolecules (ISSN 2218-273X).

Deadline for manuscript submissions: 4 June 2018

Special Issue Editor

Guest Editor
Dr. Pedro J. Ballester

INSERM, France
Website | E-Mail
Interests: computational drug design; cancer pharmacogenomic modelling; biomarker discovery; applied machine learning

Special Issue Information

Dear Colleagues,

Machine Learning (ML) has become a crucial component of early drug discovery. This research area has been fuelled by two main factors. The first is the fast-growing availability of relevant experimental data. Examples of such data are bioactivities between molecules of known chemical structure and non-molecular targets (cell lines, mice models, etc.), binding affinities of such molecules against macromolecular targets or X-ray crystal structures of proteins acting as drug targets. This trend has been catalysed by the development of community resources (e.g., ChEMBL, PubChem or PDB to name a few) that curate and facilitate re-using these data sets for predictive modelling. The second factor is the easy access to high-quality implementations in R or Python of a range of ML algorithms, along with the continuous introduction of new advances (e.g., XGBoost, deep learning or conformal prediction). As a result, an increasing number of data-driven ML models are being proposed and found advantageous in some way to identify new starting points for the drug discovery process.

We invite scientists working on this area to submit their original research or review articles for publication in this Special Issue. Topics of interest include (but are not limited to) docking, QSAR, target prediction, virtual screening or lead optimization. Both application and methodology research studies are welcome.

Dr. Pedro J. Ballester
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. Biomolecules is an international peer-reviewed open access quarterly 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 650 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.


  • Predictive modelling
  • Docking
  • QSAR
  • Virtual screening
  • Lead optimization
  • Target prediction
  • Drug design

Published Papers (1 paper)

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Open AccessFeature PaperArticle The Impact of Protein Structure and Sequence Similarity on the Accuracy of Machine-Learning Scoring Functions for Binding Affinity Prediction
Biomolecules 2018, 8(1), 12; doi:10.3390/biom8010012
Received: 8 February 2018 / Revised: 9 March 2018 / Accepted: 12 March 2018 / Published: 14 March 2018
PDF Full-text (675 KB) | HTML Full-text | XML Full-text | Supplementary Files
It has recently been claimed that the outstanding performance of machine-learning scoring functions (SFs) is exclusively due to the presence of training complexes with highly similar proteins to those in the test set. Here, we revisit this question using 24 similarity-based training sets,
[...] Read more.
It has recently been claimed that the outstanding performance of machine-learning scoring functions (SFs) is exclusively due to the presence of training complexes with highly similar proteins to those in the test set. Here, we revisit this question using 24 similarity-based training sets, a widely used test set, and four SFs. Three of these SFs employ machine learning instead of the classical linear regression approach of the fourth SF (X-Score which has the best test set performance out of 16 classical SFs). We have found that random forest (RF)-based RF-Score-v3 outperforms X-Score even when 68% of the most similar proteins are removed from the training set. In addition, unlike X-Score, RF-Score-v3 is able to keep learning with an increasing training set size, becoming substantially more predictive than X-Score when the full 1105 complexes are used for training. These results show that machine-learning SFs owe a substantial part of their performance to training on complexes with dissimilar proteins to those in the test set, against what has been previously concluded using the same data. Given that a growing amount of structural and interaction data will be available from academic and industrial sources, this performance gap between machine-learning SFs and classical SFs is expected to enlarge in the future. Full article
(This article belongs to the Special Issue Machine Learning for Molecular Modelling in Drug Design)

Figure 1

Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

1) Jonathan J. Chen, Lyndsey N. Schmucker and Donald P. Visco, Jr. Pharmaceutical Machine Learning: Virtual High-Throughput Screens Identifying Economical Small Molecule Inhibitors of Complement Factor C1s.

2) Ulf Norinder, Glenn Myatt and Ernst Ahlberg. Predicting aromatic amine mutagenicity with confidence: A case study using conformal prediction.

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