DUODUOTECH

AI/ML Modeling Services

Advanced machine learning for reaction yield prediction, bio-target identification, and causal inference using PyTorch Geometric, DoWhy, Optuna, and Scikit-Learn.

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Machine Learning for Life Sciences

Modern life sciences R&D generates complex, high-dimensional data that traditional statistical methods struggle to analyze effectively. Our AI/ML modeling services apply state-of-the-art machine learning and causal inference techniques to extract meaningful insights and predictions.

We build custom models tailored to your specific research questions, from predicting experimental outcomes to identifying causal factors that drive biological processes.

Our AI/ML Services

Reaction Yield Prediction

Machine learning models to predict chemical reaction yields and optimize reaction conditions before running experiments.

  • Neural network models for yield prediction
  • Multi-task learning across reaction types
  • Uncertainty quantification for predictions
  • Transfer learning from literature data

Bio-Target Prediction

Multi-omics integration and machine learning to identify novel therapeutic targets and biomarkers.

  • Integration of genomics, transcriptomics, proteomics
  • Target-disease association scoring
  • Druggability assessment
  • Pathway and network analysis

Causal Inference for Experiments

Identify true causal relationships between experimental factors and outcomes, not just correlations.

  • Causal discovery from observational data
  • Treatment effect estimation
  • Confounding factor identification
  • Counterfactual analysis and simulation

Model Optimization & AutoML

Automated hyperparameter tuning and model selection to maximize predictive performance.

  • Bayesian optimization with Optuna
  • Neural architecture search
  • Cross-validation and ensemble methods
  • Model interpretability and explainability

Technologies & Frameworks

PyTorch Geometric

Graph neural networks for molecular and biological network modeling.

DoWhy

Causal inference library for identifying and estimating causal effects.

Optuna

Hyperparameter optimization framework for automatic model tuning.

Scikit-Learn

Comprehensive machine learning library for classical ML algorithms.

Applications

Reaction Optimization

Predict optimal reaction conditions (temperature, catalyst, solvent) to maximize yield and minimize cost and time.

Target Identification

Integrate multi-omics data to identify novel therapeutic targets with high confidence and druggability scores.

Biomarker Discovery

Identify predictive biomarkers for disease diagnosis, prognosis, and treatment response using causal ML.

Our Modeling Process

1

Problem Formulation

Define the prediction task, success metrics, and data requirements.

2

Data Preparation

Clean, integrate, and engineer features from your experimental data.

3

Model Development

Train and optimize ML models using state-of-the-art algorithms.

4

Validation & Deployment

Validate models rigorously and deploy for real-world predictions.

Ready to Leverage AI/ML in Your Research?

Contact us to discuss how our machine learning expertise can accelerate your drug discovery and process development programs.

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