AI Speed-Up Antibody-Based Drug R&D

  In this initial stage, AI helps you move from a biological hypothesis to a validated, druggable target.

• Literature & Data Mining: Large      language models (LLMs) and natural language processing (NLP) can      autonomously parse millions of scientific papers, patents, and clinical      trial reports to identify novel targets and build a comprehensive      biological context for a disease.
• Target Validation & Biology: AI can      analyze multi-omics data (genomics, proteomics) to confirm that a target      is relevant to the disease pathway and has a favorable expression profile.      For example, the ALADDIN project used proteomic studies to identify and      validate MANF as a potential biomarker for colorectal cancer recurrence.
• Epitope Selection: AI tools like Origin-1      or mBER can predict and prioritize specific epitopes on a target protein.      This is crucial for designing antibodies with a specific function, such as      blocking a receptor's active site for therapeutic inhibition.

  In this initial stage, AI helps you move from a biological hypothesis to a validated, druggable target.

• Literature & Data Mining: Large      language models (LLMs) and natural language processing (NLP) can      autonomously parse millions of scientific papers, patents, and clinical      trial reports to identify novel targets and build a comprehensive      biological context for a disease.
• Target Validation & Biology: AI can      analyze multi-omics data (genomics, proteomics) to confirm that a target      is relevant to the disease pathway and has a favorable expression profile.      For example, the ALADDIN project used proteomic studies to identify and      validate MANF as a potential biomarker for colorectal cancer recurrence.
• Epitope Selection: AI tools like Origin-1      or mBER can predict and prioritize specific epitopes on a target protein.      This is crucial for designing antibodies with a specific function, such as      blocking a receptor's active site for therapeutic inhibition.

    AI ensures that lab testing is high-impact and directly informs model improvement.

• High-Throughput Screening: AI models can      be trained on initial screening data to predict which candidates in a      large library are most likely to succeed, guiding the selection of the      next batch of antibodies to test in a "virtuous lab-loop". This      approach was successfully used to identify promising antibodies against      immune checkpoints like TIM3 and TIGIT.
• Direct-to-Vivo Screening: Platforms like      Manifold Bio's are designed to test millions of AI-designed binders      directly in living systems. This allows AI models to learn from      biologically relevant data, bridging the gap between in vitro success and      in vivo efficacy.

 These critical stages are about predicting a drug's behavior in the body and its viability as a commercial product.

• ADME & PK Prediction: Graph neural      networks (GNNs) can be used to predict key pharmacokinetic parameters for      complex biologics like antibody-drug conjugates (ADCs). The ADC molecule      is represented as a graph, and the GNN can predict clearance, volume of      distribution, and tissue accumulation.
• Toxicity Prediction: AI models can      integrate diverse data sources (chemical structures, biodistribution      images) to predict toxicity. Multimodal deep learning and NLP models are      being developed to predict clinical toxicity, identify toxic      substructures, and even mine patient records for rare adverse events.
• Developability Assessment: Early-stage AI      tools can predict a candidate's developability profile—its potential for      manufacturability, stability, and low immunogenicity—directly from its      sequence. In one study, AI identified poor developability profiles due to      unfavorable surface properties, enabling the early elimination of      problematic candidates.

NBRLBost has combine the novel antibody based therapeutics development technology and collaboration with AI  expert company Orchestra ,  An enterprise AI platform focused on Autonomous IT Infrastructure, providing closed-loop intelligent provisioning and auto-remediation. 

We have successfully used AI process for anti-A beta monoclonal antibody and anti-multi TAA nanobody development, which have been confirmed by wet-lab experiment to finish PCC evaluation