Rate My Professor Mahakim Newton

Newton's academic interests center on artificial intelligence, intelligent search, machine learning, data science, bioinformatics, and computer education research, with expertise spanning big data analytics, computational biology, deep learning, explainable algorithms, genomics, health informatics, and optimization. His contributions have garnered over 1,359 citations according to Google Scholar. Notable publications include 'Harnessing angular geometry in deep learning for protein–ligand binding affinity prediction' (2026, Computers in Biology and Medicine), 'Efficient drug–target affinity prediction via interaction features and parallel CNN–BiLSTM with attention' (2026), 'CDTA: Consistency-based deep learning method for drug-target affinity prediction using sequential features from protein language model' (2026), 'Artificial intelligence for template-free protein structure prediction: a comprehensive review' (2023, Briefings in Bioinformatics), 'Constraint Guided Beta-Sheet Refinement for Protein Structure Prediction' (2022), and 'A stacked meta-ensemble for protein inter-residue distance prediction' (2022). He coordinates programs such as the Bachelor of Applied Data Science and teaches courses including COMP6230 Algorithms, COMP6900 Computing Project, and STAT3800 Deterministic and Stochastic Optimisation.