RESEARCH POSITIONS
Two
post-doctoral research positions in anti-cancer biologic design using
explainable AI and generative AI
Two
post-doctoral research positions are available in AI-inspired drug design in
the Biomedical Computing Group headed by Professor Jagath Rajapakse (https://personal.ntu.edu.sg/asjagath/) at Nanyang Technological University, Singapore, for
a period of three years starting from 1 July 2025.
The
project investigates the design of biologics (peptides and antibodies) as
anticancer therapeutic agents by using eXplainable AI
(XAI) and generative AI (genAI). First, we build
predictive AI models such as large language models (LLM) for predicting binding
affinities of biologics. Second, we use XAI approaches such as integrated
gradients for identifying the features of predictive models and the mechanism
of action of biologics. Third, using these features as constraints, we will use
genAI techniques such as LLM and diffusion models to
generate biologics with anti-cancer properties. The candidate will develop
necessary predictive AI, XAI and genAI methods for
design of anti-cancer biologics.
The
postdoctoral candidate is to have a PhD in a related field and experience in
deep learning architectures and frameworks like Pytorch/Tensorflow. Candidates with Master degrees with a strong
related background will also be considered. Interested candidates must email
their CVs to asjagath@ntu.edu.sg. Only
shortlisted candidates will be notified.
References:
1. C. Wang,
G. A. Kumar, and J. C. Rajapakse, “Drug discovery and mechanism prediction with
explainable graph neural networks,” Scientific Reports, Vol. 15, Issue 1, pp.
179, January 2025, IF = 3.8, DOI:
https://www.nature.com/articles/s41598-024-83090-3
2. C. Wang, H. H. Ong, S. Chiba, J. C. Rajapakse, “GLDM: Hit
molecule generation with constrained graph latent diffusion model,” Briefings
in Bioinformatics, 2024, Volume 25,
Issue 3, May 2024, IF = 6.8, bbae142, DOI: 10.1093/bib/bbae142