Special Issue "Machine Learning Applications in Polymeric Biomaterials"

A special issue of Polymers (ISSN 2073-4360). This special issue belongs to the section "Biomacromolecules, Biobased and Biodegradable Polymers".

Deadline for manuscript submissions: closed (25 August 2023) | Viewed by 3545

Special Issue Editors

Electrical Engineering Department, College of Engineering, Qatar University, PO. Box 2713 Doha, Qatar
Interests: bionanomaterials; bionanotechnology; machine learning; drug discovery; deep learning
Department of Biomedical Engineering, Military Institute of Science and Technology (MIST), Dhaka, Bangladesh
Interests: biomaterials; biocomposites; biofabrication; biomechanics; biomedical implants; nanomaterials; nanocomposites; nanotechnology; rapid prototyping technology; rehabilitation engineering; stem cells; tissue engineering
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Special Issue Information

Dear Colleagues,

Polymers are the largest and most versatile class of biomaterials extensively applied for therapeutic applications. From natural to synthetic polymers, the possibilities to design and modify their physical–chemical properties make these systems of great interest in a wide range of biomedical applications as diverse as drug delivery systems, organ-on-a-chip, diagnostics, tissue engineering, and so on.

While there is constant evolution and innovation in materials with time, biomaterials research has been hampered by the relatively long development period required. With rapid advances in computational power, machine learning (ML) has boomed as an effective tool to discover new materials. The combination of machine learning with high-throughput theoretical predictions and high-throughput experiments has shifted from the traditional trial and error paradigm to a data-driven paradigm. In the field of polymers, ML has also found its applications in finding new materials with the desired performance.

The purpose of this Special Issue is to highlight recent achievements from biomaterials discovery and characteristic prediction to final applications in the field of biomedicine.

Dr. Muhammad E. H. Chowdhury
Prof. Dr. Md. Enamul Hoque
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. Polymers 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.


  • machine learning framework for polymer discovery
  • machine learning in polymerization process
  • prediction of organic materials
  • machine learning for the nanomaterials–biology interface
  • prediction of tensile strength of polymer
  • prediction in the biomedical effects of nanomaterial application
  • machine learning for drug discovery

Published Papers (1 paper)

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A Review of Biomaterials and Associated Performance Metrics Analysis in Pre-Clinical Finite Element Model and in Implementation Stages for Total Hip Implant System
Polymers 2022, 14(20), 4308; https://doi.org/10.3390/polym14204308 - 13 Oct 2022
Cited by 6 | Viewed by 3114
Total hip replacement (THR) is a common orthopedic surgery technique that helps thousands of individuals to live normal lives each year. A hip replacement replaces the shattered cartilage and bone with an implant. Most hip implants fail after 10–15 years. The material selection [...] Read more.
Total hip replacement (THR) is a common orthopedic surgery technique that helps thousands of individuals to live normal lives each year. A hip replacement replaces the shattered cartilage and bone with an implant. Most hip implants fail after 10–15 years. The material selection for the total hip implant systems is a major research field since it affects the mechanical and clinical performance of it. Stress shielding due to excessive contact stress, implant dislocation due to a large deformation, aseptic implant loosening due to the particle propagation of wear debris, decreased bone remodeling density due to the stress shielding, and adverse tissue responses due to material wear debris all contribute to the failure of hip implants. Recent research shows that pre-clinical computational finite element analysis (FEA) can be used to estimate four mechanical performance parameters of hip implants which are connected with distinct biomaterials: von Mises stress and deformation, micromotion, wear estimates, and implant fatigue. In vitro, in vivo, and clinical stages are utilized to determine the hip implant biocompatibility and the unfavorable local tissue reactions to different biomaterials during the implementation phase. This research summarizes and analyses the performance of the different biomaterials that are employed in total hip implant systems in the pre-clinical stage using FEA, as well as their performances in in vitro, in vivo, and in clinical studies, which will help researchers in gaining a better understanding of the prospects and challenges in this field. Full article
(This article belongs to the Special Issue Machine Learning Applications in Polymeric Biomaterials)
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