This perspective surveys emerging strategies and computational models that are redefining enzyme engineering and examines the opportunities and challenges on the path toward truly on-demand enzyme design.
Abstract
Artificial intelligence is rapidly transforming everyday life and driving major advances across science. De novo enzyme design is an area of particular promise, with implications for medicine, biotechnology, and industry. Recent applications of AI-enhanced methodologies have yielded a range of enzymes with catalytic activities that were previously unattainable through conventional approaches. This perspective surveys emerging strategies and computational models that are redefining enzyme engineering and examines the opportunities and challenges on the path toward truly on-demand enzyme design.
The foundation of polymer databases is built upon to critically examine the performance and applicability of current encoding strategies across different use cases and focuses on two major AI application domains, property prediction and inverse design, to evaluate the strengths, weaknesses and suitable scenarios for va...
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, M. Rizwan, Vivian Andoh et al.· Journal of Agricultural and...· 0 citations
Artificial intelligence (AI) has advanced rapidly in recent years, offering transformative opportunities across chemistry and the biological sciences. Yet effectively integrating these tools into everyday research remains a key challenge. This mini review summarizes recent advances in applying AI to RNA-targeted drug d...
Qingwen Chen· Frontiers in Chemical Biolog...· 0 citations
Generative artificial intelligence (AI) is reshaping molecular design, but many outputs are still judged by proxy metrics that do not establish whether proposed compounds can be synthesized, tested and advanced. This review reframes the field around 'synthetic reality': the extent to which AI-generated molecules surviv...
Sebastián O. Simonetti, Aldana B. Moroni, M. Zanardi et al.· Drug Discovery Today· 0 citations
The monograph introduces state-of-the-art AI methodologies, including scientific machine learning, physics-constrained models, graph-based and generative approaches, and decision-oriented frameworks, and demonstrates their application to molecular and interface design, data integration, and reaction network analysis. C...
How solid-phase peptide synthesis, genetically encoded libraries, and high-throughput selection and screening enable systematic exploration of vast, noncanonical landscapes largely inaccessible to traditional engineering is discussed.
Filip Buchel, V. G. Giacobelli, K. Hlouchová· TIBS -Trends in Biochemical...· 0 citations
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