ReviewMolecules (Basel, Switzerland)2024
Revolutionizing Molecular Design for Innovative Therapeutic Applications through Artificial Intelligence.
Review in Molecules (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed.
- ReLink-PyB: an adapted REINVENT-based framework for non-DSM PfDHODH inhibitor discovery.Scientific reports · 2026Article
- Thermostability Engineering in Therapeutic Antioxidant Enzymes: From Molecular Fundamentals to Oxidative Stress Applications.International journal of molecular sciences · 2026Review
- Coordination chemistry-enabled drug delivery systems: metal-ligand platforms for controlled release and targeted therapeutics.Journal of nanobiotechnology · 2026Review
- MAMMAL - Molecular Aligned Multi-Modal Architecture and Language for biomedical discovery.npj drug discovery · 2026Article
- AI-Resolved Protein Energy Landscapes, Electrodynamics, and Fluidic Microcircuits as a Unified Framework for Predicting Neurodegeneration.International journal of molecular sciences · 2026Review
- Molecular docking in histological biomarker discovery and disease modeling: techniques, validation, and translational perspectives.Journal of molecular histology · 2026Review
- Protein Engineering and Drug Discovery: Importance, Methodologies, Challenges, and Prospects.Biomolecules · 2025Review
- Formulation of Recombinant Therapeutic Proteins: Technological Innovation, Regulations, and Evolution Towards Buffer-Free Formulations.Pharmaceutics · 2025Review
- Modification and applications of glucose oxidase: optimization strategies and high-throughput screening technologies.World journal of microbiology & biotechnology · 2025Review
- Overcoming translational barriers in RNA-protein docking: enhancing computational accuracy for targeted drug discovery.Future medicinal chemistry · 2025Review
- Enhancing the Protein Stability of an Anticancer VHH-Fc Heavy Chain Antibody through Computational Modeling and Variant Design.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Stability and functional consequences of disulfide bond engineering in Aspergillus flavus uricase.Scientific reports · 2025Article
- Dual GSK3β/SIRT1 modulators for Alzheimer's: mechanisms, drug discovery and future perspectives.Frontiers in pharmacology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
The field of computational protein engineering has been transformed by recent advancements in machine learning, artificial intelligence, and molecular modeling, enabling the design of proteins with unprecedented precision and functionality. Computational methods now play a crucial role in enhancing the stability, activity, and specificity of proteins for diverse applications in biotechnology and medicine. Techniques such as deep learning, reinforcement learning, and transfer learning have dramatically improved protein structure prediction, optimization of binding affinities, and enzyme design. These innovations have streamlined the process of protein engineering by allowing the rapid generation of targeted libraries, reducing experimental sampling, and enabling the rational design of proteins with tailored properties. Furthermore, the integration of computational approaches with high-throughput experimental techniques has facilitated the development of multifunctional proteins and novel therapeutics. However, challenges remain in bridging the gap between computational predictions and experimental validation and in addressing ethical concerns related to AI-driven protein design. This review provides a comprehensive overview of the current state and future directions of computational methods in protein engineering, emphasizing their transformative potential in creating next-generation biologics and advancing synthetic biology.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.