Neorabio-Home-AI-Based Site Prediction Service

Current Location: HOME> Technical Services> AI-Driven Drug R&D Services> AI-Based Site Prediction> AI-Based Site Prediction Service - Neorabio

AI-Based Site Prediction Service - Neorabio

Neorabio provides AI-driven site prediction services for research programs that require precise identification of functional regions in proteins or other biomacromolecules. Computational approaches powered by machine learning have reshaped how researchers assess ligand pockets, catalytic residues, and immune-relevant surfaces. Early demonstrations—such as DeepSite reported by Jiménez et al. (2017)—showed that 3D convolutional models can learn structural features that traditional rule-based methods often miss. Neorabio leverages these methodological advances within a controlled, reproducible workflow to deliver high-quality functional site predictions for drug discovery and molecular design.

About Service

Our site-prediction pipeline combines deep learning architectures with classical structural analysis tools. The AI component is trained to detect spatial and physicochemical signatures associated with functional sites, while the structural-bioinformatics layer provides geometric checks, evolutionary validation, and additional filtering. Insights from broader analyses of deep learning in structural modeling, including work summarized by Gao and Skolnick (2020), guide how Neorabio configures features, model architectures, and benchmarking strategies. High-performance computing resources, curated structural datasets, and standardized QC procedures ensure consistent and biologically credible outputs across diverse protein systems.

Key Advantages

● Accurate detection of binding pockets: AI-derived spatial features complement geometric and physicochemical analysis.
● Catalytic and functional residue identification: Models incorporate evolutionary and local-environment cues.
● Mutation-sensitive region mapping: Highlighting structural areas vulnerable to functional disruption.
● Epitope discovery for antibody development: Surface-pattern recognition supports vaccine and antibody projects.
● Customizable pipelines: Adaptable for enzymes, receptors, membrane proteins, antibodies, or multimeric complexes.

Applications

● Target assessment and binding-site discovery: Supporting early-stage hit generation and selectivity analysis.
● Lead optimization and rational design: Integrating predicted hotspots into ligand engineering strategies.
● Antibody and epitope screening: Identifying accessible, immunologically relevant structural motifs.
● Functional impact assessment of mutations: Evaluating how amino-acid substitutions alter interaction regions.
● Mechanistic hypothesis building: Interpreting structure–function relationships at the residue or domain level.

Workflow

Submit Structure → Structure Preprocessing → Parameter Setup → Docking Simulation → Result Analysis → Report Delivery

References

1.Jiménez J., Doerr S., Martínez-Rosell G., Rose A.S., De Fabritiis G. DeepSite: protein-binding site predictor using 3D-convolutional neural networks. Bioinformatics. 2017;33(19):3036–3042. doi:10.1093/bioinformatics/btx350
2.Gao M., Skolnick J. Deep learning in protein structural modeling and design. Briefings in Bioinformatics. 2020;21(1):37–47. doi:10.1093/bib/bby097
3.Torng W., Altman R.B. High precision protein functional site detection using 3D convolutional neural networks. Bioinformatics. 2019;35(9):1503–1512. doi:10.1093/bioinformatics/bty898
4.Gainza P., et al. Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning. Nature Methods. 2020;17(2):184–192. doi:10.1038/s41592-019-0666-6

Inquiry Center

Neorabio's bioinformatics and AI modeling teams combine domain expertise in protein structures with practical machine-learning engineering. Model outputs are reviewed using structural metrics, evolutionary conservation data, geometric validation, and interpretability analyses. Deliverables include ranked site predictions, annotated 3D visualizations, residue-level confidence scores, and recommendations for downstream experiments or computational follow-up. This integrated approach ensures that predicted sites are both scientifically grounded and actionable for drug discovery and antibody development.

NEORABIO

Technical Services

About Service

Key Advantages

Applications

Workflow

References

Inquiry Center