Title:Drug Design and Disease Diagnosis: The Potential of Deep Learning
Models in Biology
Volume: 18
Issue: 3
Author(s): Sarojini Sreeraman, Mayuri P. Kannan, Raja Babu Singh Kushwah, Vickram Sundaram*, Alaguraj Veluchamy, Anand Thirunavukarasou*Konda Mani Saravanan*
Affiliation:
- Department of Biotechnology, Saveetha School of Engineering, Saveetha Institute of Medical, and Technical Sciences
(SIMATS), Thandalam, Chennai, 602105, Tamil Nadu, India
- B-Aatral Biosciences Private Limited, Bangalore,
560091, Karnataka, India
- B-Aatral Biosciences Private Limited, Bangalore,
560091, Karnataka, India
Keywords:
Drug-target interaction, deep learning models, diagnosis, cancer drugs, resources, databases.
Abstract: Early prediction and detection enable reduced transmission of human diseases and provide
healthcare professionals ample time to make subsequent diagnoses and treatment strategies. This, in
turn, aids in saving more lives and results in lower medical costs. Designing small chemical molecules
to treat fatal disorders is also urgently needed to address the high death rate of these diseases worldwide.
A recent analysis of published literature suggested that deep learning (DL) based models apply more
potential algorithms to hybrid databases of chemical data. Considering the above, we first discussed the
concept of DL architectures and their applications in drug development and diagnostics in this review.
Although DL-based approaches have applications in several fields, in the following sections of the article,
we focus on recent developments of DL-based techniques in biology, notably in structure prediction,
cancer drug development, COVID infection diagnostics, and drug repurposing strategies. Each
review section summarizes several cutting-edge, recently developed DL-based techniques. Additionally,
we introduced the approaches presented in our group, whose prediction accuracy is relatively comparable
with current computational models. We concluded the review by discussing the benefits and drawbacks
of DL techniques and outlining the future paths for data collecting and developing efficient computational
models.