README.md

Steps We Have Performed

Step 1: Lipophilicity Prediction

• Objective: Assess the lipophilicity (hydrophobic or hydrophilic characteristics) of drug-like molecules, as it affects pharmacokinetics, including absorption, distribution, and solubility.

• Procedure:

  • Data Preparation: Use a molecular dataset with known lipophilicity values. Ensure molecules are represented using SMILES notation.
  • Modeling: Deploy a pre-trained OpenVINO model optimized for regression tasks, fine-tuned to predict lipophilicity.
  • Deployment on OpenShift: Containerize the model and deploy it on RedHat OpenShift for scalable predictions. The platform supports container orchestration, ideal for real-time lipophilicity assessments.
  • Output: Predicted lipophilicity scores allow for screening molecules based on their potential suitability in drug development.

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Step 2: Binding Affinity Prediction

• Objective: Evaluate the binding affinity between molecules and biological targets, which is crucial for efficacy.

• Procedure:

  • Data Preparation: Gather a dataset with labeled binding affinities for various protein-ligand pairs. Represent ligands in SMILES and proteins in appropriate 3D formats.
  • Modeling: Use OpenVINO for accelerated processing and inference to predict binding affinity. Models like deep docking networks can estimate molecule-target binding efficacy.
  • Deployment: Deploy the model on OpenShift to handle large batches, facilitating drug library screening.
  • Output: Generate binding affinity scores, ranking molecules by their likelihood of effective binding.

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Step 3: Molecule Generation and Scoring

• Objective: Generate new molecules based on an initial molecular scaffold and score them for potential efficacy.

• Procedure:

  • Scaffold Selection: Define an initial molecule or scaffold with promising characteristics.
  • Molecule Generation: Using RDKit with OpenVINO, create scaffold variations. RDKit modifies functional groups, with OpenVINO accelerating inference.
  • Scoring: Score generated molecules on metrics like lipophilicity, binding affinity, and other pharmacokinetic properties using previously deployed models.
  • Deployment: Containerize and deploy on OpenShift for efficient, parallel processing.
  • Output: Rank and select top candidates with the highest scores for further analysis.

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Step 4: Reaction Prediction and SMILES Visualization

• Objective: Predict chemical reactions and visualize the products to assess synthetic feasibility and optimize drug synthesis.

• Procedure:

  • Reaction Prediction: Use OpenVINO-accelerated models trained for reaction prediction. Input SMILES representations of reactants to predict potential products.
  • Visualization: OpenVINO generates visual representations of SMILES for chemists to analyze.
  • Deployment: Deploy workflows on OpenShift for real-time feedback in synthesis planning.
  • Output: Predicted products and visual representations support feasible reaction selection for drug synthesis.

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Step 5: Reaction Prediction with RAG (Retrieval-Augmented Generation) Integration

• Objective: Enhance reaction prediction accuracy by combining external data retrieval with generative modeling. RAG retrieves examples of similar reactions, enriching prediction quality with contextually relevant data.

• Procedure:

  • Data Retrieval:
    • Objective: Retrieve similar reactions and synthesis pathways from a database for historical context.
    • Method: Qdrant stores reaction data as 4096-dimensional vectors using RDKit’s GetMorganGenerator to capture molecular structures.
    • Implementation: A reaction prediction initiates a query to Qdrant, retrieving similar reactions based on cosine similarity, adding context to predictions.
  • Reaction Prediction:
    • Objective: Generate reaction predictions with OpenVINO, incorporating RAG context.
    • Model Optimization: Convert the prediction model to OpenVINO format for accelerated inference, creating predictions in real-time.
    • Procedure:
      • Prepare SMILES of reactants and products as 8192-dimensional input vectors.
      • OpenVINO infers a likelihood score, augmented by RAG for context-rich insights.
  • Deployment on OpenShift:
    • Objective: Deploy the RAG-enabled system on RedHat OpenShift for scalable, efficient processing.
    • Advantages:
      • Scalability: OpenShift supports large-scale retrieval and generation workflows, enabling concurrent requests.
      • Real-time Access: OpenShift deployment with Gradio’s web interface supports remote access for synthesis planning.
    • Implementation: Containerize the workflow (Qdrant retrieval, OpenVINO prediction, Gradio interface) for scalable resource allocation.
  • Output:
    • Enhanced Predictions: RAG with OpenVINO produces contextually enriched reaction predictions.
    • Synthesized Pathways: Displayed Qdrant data gives researchers feasible reaction pathways, supporting practically informed decisions.

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