This review examines how artificial intelligence (AI) and automation are reshaping enzyme engineering from empirical trial‑and-error toward data-driven, closed-loop design and provides a roadmap for advancing AI-guided and autonomous enzyme engineering.
Abstract
Natural enzymes often fail to meet industrial demands for catalytic efficiency, stability, and substrate specificity, creating a critical bottleneck in biomanufacturing. This review examines how artificial intelligence (AI) and automation are reshaping enzyme engineering from empirical trial‑and‑error toward data-driven, closed-loop design. We trace AI development from feature-engineered machine learning to supervised deep learning and self-supervised protein language models, and automation from standalone task execution to cascade integration and biofoundry-enabled build-test workflows. Their convergence is analyzed through a stage-based autonomy framework, highlighting the transition from semi-automated workflows to conditional and high-autonomy DBTL systems. Recent studies demonstrate that AI-guided prediction, automated experimentation, and active learning can accelerate enzyme optimization; however, key barriers remain, including biased datasets, limited out-of-distribution generalization, weak mechanistic interpretability, automation interoperability constraints, and unresolved multi-objective trade-offs. We discuss future directions involving FAIR-compliant data infrastructure, hybrid sequence-structure-physics models, modular automation platforms, and autonomous closed-loop systems. By integrating historical evolution, representative case studies, success and failure analysis, and practical bottlenecks, this review provides a roadmap for advancing AI-guided and autonomous enzyme engineering.
This review examines enzyme engineering from classical methods to AI-assisted biocatalyst development, highlighting key advances, challenges, and emerging trends in autonomous laboratories, sustainable biocatalysis, and computational protein design.
Mati Ullah, Muhammad Rizwan, Vivian Andoh et al.· Journal of Agricultural and...· 0 citations
Chemical research is no longer confined strictly to the lab bench or trial and error. Artificial
intelligence is transforming the field, helping to predict molecular behavior, find potential designs,
and automate certain aspects of the discovery process, introducing a new kind of intuition. Over
the past decade, advances in machine learning, natural language processing, robotics, and automation
have enabled new areas of research. These are broadening the applications for retrosynthetic analysis,
reaction optimization, and computer-aided synthetic planning. This study examines the evolution of
computer-aided synthesis, describing its development from rule-based approaches to advanced deep
learning and hybrid systems that leverage large datasets. Thus, it focuses on AI platforms that integrate
predictive algorithms with rapidly evolving robotic systems. Such technologies enable rapid
hypothesis generation, reaction screening, and the improvement of synthetic methods. The review
encompasses synthesis analysis tools, recommendation algorithms, and autonomous labs that deliver
discoveries more quickly and minimize waste and environmental impact. It examines current challenges,
such as data scarcity, sporadic reporting, model interpretability, and practical applications.
More broadly, the need for sustainable, collaborative research has increased, and cross-border work
through cloud-based laboratories and shared databases enables chemists worldwide to share resources.
The review identifies beneficial trends and ongoing challenges, with a view to providing opportunities
for AI to make chemistry greener, accelerate discovery, and improve decision-making across academic
and industrial settings. AI is not replacing chemists but rather enhancing creativity and intuition,
bringing together research that traditional methods would never have allowed, on a scale never before
possible without AI.
Rizvee Ahmad Samir, Yu-Meng Zhang, Zi-Shan Xu et al.· Letters in Organic Chemistry· 0 citations
The integration of artificial intelligence and machine learning into chemical engineering represents a paradigm transformation that fundamentally reconceptualizes practice across molecular, process, and industrial scales. This comprehensive narrative review synthesizes recent advances in AI/ML methodologies, including graph neural networks, physics-informed neural networks, deep reinforcement learning, and generative artificial intelligence – and critically evaluates their applications spanning molecular property prediction, catalyst design, pharmaceutical development, reactor optimization, process control, and sustainability initiatives. Contemporary AI/ML approaches demonstrate unprecedented capabilities in navigating complex multiscale phenomena while maintaining computational tractability and physical interpretability. Landmark achievements include 71% reduction in experimental iterations for reaction optimization through deep reinforcement learning, 98% accuracy in predictive maintenance using LSTM-based fault detection, sub-1% prediction errors in virtual metrology for semiconductor manufacturing, and substantial improvements incarbon capture efficiency through machine learning-guided materials discovery. Physics-informed neural networks address the critical challenge of plant-model mismatch by synergistically integrating mechanistic knowledge with data-driven learning, enabling extrapolation beyond training domains while respecting conservation laws. Explainable AI techniques, particularly SHAP analysis, enhance operational acceptance by 52% in safety-critical applications through transparent decision-making pathways. Despite remarkable progress, persistent challenges remain in data quality and standardization, model interpretability for regulatory compliance, computational scalability for real-time control, and integration with legacy industrial infrastructure. The review identifies transformative future directions including multi-modal learning frameworks, transfer learning for data-scarce applications, quantum machine learning for molecular design, and human-AI collaborative systems. Successful deployment demands interdisciplinary collaboration uniting chemical engineering domain expertise with computational intelligence, guided by principles of transparency, reproducibility, and responsible innovation to address sustainability imperatives while maintaining operational excellence and safety in chemical manufacturing.
Fatemeh Valizadeh Hajidehi, Azam Mina· Chemical and Process Enginee...· 0 citations
Drug discovery is widely known to be an extremely complex and costly process due to its cost framework, long cycles, and high turnover rates. Robotics and Artificial Intelligence (AI) introduce the world to a disruptive, mechanized, and data analytics paradigm that can accelerate the initial stages of drug development.
accelerate the initial stages of drug development.
Materials and Methods
The results show that AI and robotics improve assay reproducibility, optimize chemical reactions, and enable the conversion of a hit into a lead. Automated procedures lead to greater data reliability, less experimental time and cost, and higher success rates. Opening new chemical and biological spaces that were previously unexplored, faster discovery timelines, and an even more efficient allocation of resources are demonstrated in both industrial and academic examples.
The collaboration of robotics, AIs, and human expertise creates a hybrid workflow, where routine work is automated, freeing investigators to focus on creative and decision-making work. The ongoing barriers include technical constraints, model interpretation issues, financial constraints, legal compliance and ethical considerations, and personnel adjustments.
The field of drug discovery is being radically transformed by robotics and AI, allowing scalable, efficient and innovative approaches. A trend towards fully autonomous, self-learning laboratories, including generative AI, quantum computing and digital biology, is anticipated, likely to develop new therapeutics faster, more accurately and at significantly lower cost.
Aadarsh Kumar, Md Moidul Islam, Abhishek Kumar et al.· Current Artificial Intellige...· 0 citations
Synthetic biology applies engineering principles to the rational design of biological systems with the aim of producing predictable and tunable behaviour. Although the field's conceptual foundations and core technologies are well established, the recent simultaneous maturation of Quality by Design (QbD), artificial intelligence (AI)-assisted biological design, and automated biofoundry workflows is beginning to outline a more replicable pathway from laboratory innovation to industrial-scale circular biomanufacturing. This review argues that the convergence of these three elements, rather than any one alone, characterises the current phase of the field. We examine how the Design-Build-Test-Learn (DBTL) cycle is being transformed from a research heuristic into a systematic industrial development framework; show how shared toolsets now transfer across microbial, plant, and animal systems to enable a holistic bioeconomy; benchmark synthetic biology-derived products against conventional alternatives where techno-economic and life-cycle data permit; and examine scale-up, regulatory, and societal bottlenecks through recent market-scale case studies in which these bottlenecks have been navigated in practice. We also contrast EU and US regulatory frameworks to show how policy divergence shapes technology adoption. We conclude by identifying what the coming decade of convergent synthetic biology must deliver to support a circular bioeconomy at the scale the 2030 Agenda demands.
Carlos Belloch-Molina, Francisco Vitor Santos da Silva, John P. Morrissey et al.· Biotechnology Advances· 0 citations
Results indicate that supervised machine learning can help guide the construction of high-value enzyme libraries with expanded catalytic scope, and suggest that supervised machine learning can help guide the construction of high-value enzyme libraries with expanded catalytic scope.
Ravi G. Lal, Jason Yang, Ziyan Zhang et al.· bioRxiv· 0 citations