AI-Enabled Molecular Engineering: Accelerating the Design of Therapeutics, Materials and Bio-Based Products

Andrew B. Hargadon

AI-Enabled Molecular Engineering: Accelerating the Design of Therapeutics, Materials and Bio-Based Products

Author(s)	Andrew B. Hargadon
Country	United States
Abstract	Artificial intelligence is transforming molecular engineering by enabling researchers to predict, design and optimise molecules, materials and biological products at unprecedented speed and scale. Traditional molecular discovery relies heavily on experimental screening, iterative synthesis and computational modelling, which can require substantial time and resources. AI-enabled molecular engineering integrates machine learning, deep learning, generative models, molecular simulation, multimodal data analysis and automated experimentation to create more efficient design-to-discovery pipelines. This paper examines the emerging role of AI in the engineering of therapeutics, advanced materials and bio-based products. It explores applications including drug discovery, protein design, molecular property prediction, de novo molecular generation, catalyst design, battery materials, polymers, biomaterials, enzymes and sustainable biomanufacturing. Particular attention is given to generative artificial intelligence, graph neural networks, foundation models, reinforcement learning and active-learning approaches for navigating complex molecular design spaces. The paper proposes an integrated AI-enabled molecular engineering framework connecting data generation, molecular representation, predictive modelling, generative design, simulation, experimental validation and iterative optimisation. The study also examines challenges involving data quality, molecular representation, explainability, uncertainty, experimental reproducibility, model generalisation and computational cost. The paper argues that the greatest potential of AI lies not in replacing laboratory experimentation but in creating closed-loop discovery systems in which artificial intelligence prioritises promising candidates, automated platforms perform experiments and new experimental data continuously improve computational models. Such systems could substantially accelerate the development of medicines, high-performance materials and sustainable bio-based products while reducing experimental waste and resource consumption.
Keywords	Artificial Intelligence, Molecular Engineering, Drug Discovery, Materials Design, Generative AI, Protein Engineering, Machine Learning, Molecular Simulation, Bio-Based Products, Computational Chemistry.
Field	Engineering
Published In	Volume 7, Issue 1, January-February 2025
Published On	2025-02-22

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American Journal of Advanced Multidisciplinary Research and Innovation

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AI-Enabled Molecular Engineering: Accelerating the Design of Therapeutics, Materials and Bio-Based Products

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