Madiha Ahmed Masters Thesis Defence Thursday, February 1st, 1:00 PM.

Madiha Ahmed, a Master of Science (in Statistics) candidate in the Department of Mathematics and Statistics, will defend her M.Sc. Thesis titled Attention-Based Generative Model in Deep Evolutionary Learning: A Multi-Objective Approach to Multi-Target SMILES Fragment-Based Drug Design for Cancer on Thursday, February 1st, 2024 at 1:00 pm online on Microsoft Teams.

Students (both graduate and undergraduate) as well as other members of the Brock Community are invited to attend. If you are interested in the presentation, please contact Neil Marshall at for the teams link.


Cancer remains a global health challenge, necessitating novel drug discovery methods. This graduate thesis introduces two innovative computational frameworks for multitarget drug design in cancer therapy firstly, by integrating Deep Evolutionary Learning (DEL) with a Transformer-based model. Departing from the traditional use of Variational Autoencoder (VAE), this research employs a Transformer-based generative model, capitalizing on its superior ability to capture long-range dependencies within molecular sequences to develop an understanding of the complex molecular grammar. Secondly, the research further evaluates the efficacy of a more granular fragmentation method than the one originally employed in DEL. These two proposed modifications of DEL: (i) Transformer-based model integrated in the original DEL framework and (ii) a fragmentation technique in finer granularity incorporated in the original DEL framework, are each evaluated and compared against the original DEL framework, the benchmark, in their molecular generative capabilities of targeting multiple proteins in cancer progression. In essence, the Transformer’s parallel processing capabilities enhance the drug design efficiency in terms of enhancing the diversity of novel and valid population samples produced and generating the highest-ranked novel molecule with the most optimal set of protein-ligand binding affinities. By optimizing the fragmentation technique, it is observed that it also performs well in maintaining a high novelty and validity of molecular compounds and interestingly, in drug design tasks involving specification of the off-targets, it produces a higher number of novel compounds that satisfy the objective thresholds compared to the benchmark. Overall, we believe that these are two groundbreaking approaches that can be explored for developing efficient cancer treatments, and can also offer potential solutions for other diseases requiring multi-target interventions.

The examination committee includes Melanie Pilkington, Chair; S. Ejaz Ahmed and Yifeng Li, Co-Supervisors; Jinqiang Hou, External Examiner (Lakehead University); and Tianyu Guan and Betty Ombuki-Berman, Committee Members.