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

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

Deadline for manuscript submissions: 30 April 2024 | Viewed by 317

Special Issue Editors

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 Issues, Collections and Topics in MDPI journals
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
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Life sciences heavily rely on data collected in different ways, for example, via experimental work, medical observations, or computer simulations, etc. 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, with a significant among being available in public databases (i.e. 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 demand in computational approaches to exploit such an amount of data along with its complexity, utilizing data mining and visualization techniques, machine/deep learning algorithms, and generative models in the process.

Within this context, this Special Issue aims 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, bio/chemoinformatics, machine learning, deep learning, and generative models. Experimental and theoretical research studies are welcome; multi-disciplinary approaches are particularly encouraged.

We look forward to your contributions.

Dr. Carmen Cerchia
Prof. Dr. Antonio Lavecchia
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 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.


  • drug discovery
  • medicinal chemistry
  • chemoinformatics
  • bioinformatics
  • machine learning
  • deep learning

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Published Papers

This special issue is now open for submission.
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