Special Issue "Applications of Machine Learning and Bioinformatics to Antibody Discovery"

A special issue of Antibodies (ISSN 2073-4468). This special issue belongs to the section "Antibody Discovery and Engineering".

Deadline for manuscript submissions: 20 December 2023 | Viewed by 319

Special Issue Editor

Altus Enterprises, Billerica, MA, USA
Interests: biologics; antibody discovery; computational sciences; predictive modeling; machine learning; rational design

Special Issue Information

Dear Colleagues, 

Bioinformatics and machine learning approaches hold promise for the optimization and discovery of therapeutic antibody candidates. The advent of high-throughput screening capabilities and the increasing number of experimentally solved antibody structures and next-generation sequencing projects have enabled bioinformatics (statistical or structure-guided) and machine learning (classical or deep learning) methods to predict and improve various antibody properties, including affinity, specificity, thermal stability, or solubility, which have a direct impact on the safety and efficacy of the therapeutic. Despite this exciting progress, several challenges remain: (1) the strong tradeoffs exhibited by the antibody properties warrant several rounds of optimization in the initial discovery phase, which makes antibody development challenging; (2) current methods are not set up to dynamically and incrementally learn from the iterative cycles of data generation, which can increase the cost and decrease the speed for lead candidate generation; (3) the applicability of these methods across different antigen–antibody systems is rarely tested; and (4) the de novo design of epitope-specific antibodies with drug-like properties is still an unsolved problem. There is cautious optimism, however, that in silico antibody discovery will overcome these challenges and emerge as a powerful method complementary to in vivo (immunization) and in vitro (e.g., phage display) discovery methods.

This Special Issue will focus on original research articles and reviews pertaining to advances in computational methods for antibody design, structure prediction, antigen–antibody interactions, NGS data analysis, antibody property (single or multiple) prediction and/or improvement and de novo discovery.

Dr. Kannan Tharakaraman
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 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. Antibodies 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 1600 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.


  • antibody discovery
  • computational design
  • machine learning
  • deep learning
  • structural biology
  • docking
  • affinity enhancement
  • developability

Published Papers

This special issue is now open for submission.
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