At CD ComputaBio, we understand the critical importance of accurate and efficient synthesis prediction in the design and development of novel molecules. Our synthesis prediction service leverages the power of artificial intelligence, specifically machine learning and deep learning algorithms, to predict the most viable synthetic routes for target molecules with exceptional precision. By analyzing vast amounts of chemical data, reaction mechanisms, and historical synthesis pathways, our AI algorithms can rapidly generate and evaluate potential synthetic routes, significantly reducing the time and resources required for experimental synthesis. Whether you are a research scientist in the pharmaceutical industry, a materials engineer, or a chemist in academia, our synthesis prediction service is designed to streamline and optimize the synthesis planning process, leading to faster innovation and discovery.

Our Services

Fig 1: Classified machine learning approaches into supervised and unsupervised learnings into respective categories.

Custom Product Synthesis

  • Catalyst Screening and Synthesis
  • Drug Molecule Synthesis
  • API and Intermediate Synthesis
  • Molecular Building Blocks Synthesis
  • Compound Library Synthesis
Fig 2: Chemical synthesis prediction service

Chemical Reaction Path Formation

  • Forward Synthesis Prediction
  • Retrosynthetic Prediction
  • Reaction Condition Recommendation
  • Screening and Optimization of Reaction Conditions
  • Design and Exploration of New Molecular Routes
Fig 3: Chemical synthesis prediction service

Scale-Up Synthesis

  • Scale-Up Synthesis and Process Optimization of Preclinical Candidate Compounds

Our Analysis Methods

Through predictive modeling techniques, we can simulate various reaction scenarios, predict compound properties, and optimize synthetic routes to minimize costs and maximize efficiency. By combining quantitative structure-activity relationship (QSAR) modeling, reaction prediction algorithms, and property prediction models, we offer a comprehensive suite of tools to support your chemical synthesis endeavors.

Our proprietary reaction prediction networks are specifically tailored for the task of synthesizing target molecules. By integrating knowledge of reaction mechanisms, chemical properties, and structural information, these networks can accurately forecast the most efficient and practical pathways for chemical synthesis.

Our synthesis prediction service is built on a foundation of state-of-the-art machine learning models trained on diverse chemical datasets. These models are capable of learning complex patterns within chemical reactions, enabling them to predict viable synthetic routes for target molecules with remarkable accuracy.

Service Highlights

Fig 4: Chemical synthesis prediction service

Enhanced Predictive Accuracy

Our AI algorithms have been trained on vast repositories of chemical data, allowing us to deliver highly accurate predictions for a wide range of reaction types, functional groups, and chemical transformations.

Fig 4: Chemical synthesis prediction service


CD ComputaBio's AI-aided chemical synthesis prediction service helps in optimizing reaction conditions, reducing reagent waste, and minimizing experimental costs.

Fig 4: Chemical synthesis prediction service

Tailored Solutions

Whether you are working on small molecule synthesis, drug design, or material synthesis, our team can tailor our AI algorithms to meet your individual needs.

Fig 5: Chemical synthesis prediction service

Continuous Innovation

By prioritizing innovation and continuous improvement, we ensure that our clients benefit from the most cutting-edge technologies and methodologies available.

In the ever-evolving landscape of chemical research and development, CD ComputaBio stands as a leader in AI-aided synthesis prediction services. With our blend of advanced AI technologies, specialized expertise, and commitment to excellence, we empower researchers and scientists to unlock new opportunities, accelerate discovery timelines, and achieve breakthroughs in molecular design. If you are interested in our services or have any questions, please feel free to contact us.


  • Selvaraj C, Chandra I, Singh S K. Artificial intelligence and machine learning approaches for drug design: challenges and opportunities for the pharmaceutical industries[J]. Molecular diversity, 2021: 1-21.


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