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Computer-Aided Chemical Drug Synthesis


Currently, the cost of bringing a single drug to the market exceeds $200 to $3 billion. A large part of this cost is attributed to two factors: the high rate of attrition of candidate molecules through clinical trials and the complexity of the drug discovery phase. Both of them require a large investment of time and resources. A stronger pipeline of preclinical drug candidates will have a beneficial impact on the downstream. Advances in computer hardware and computer methods are aimed at speeding up and improving all aspects of the typical drug discovery cycle (design-manufacture-test-analysis (DMTA)). Alfa Chemistry has applied data-driven synthetic prediction tools to accelerate the synthesis of new molecular entities and reduce the rate of failures.

Machine learning applied to search for new reactivity in computer-aided chemical drug synthesis.Figure 1. Machine learning applied to search for new reactivity in computer-aided chemical drug synthesis. (Granda, J. M.; et al. 2018)

Our Services

  • Retrosynthetic planning

Retrosynthetic planning is the most important part in the computer-aided chemical drug synthesis. It refers to breaking the target molecule into new small molecules (i.e. precursors) starting from the end point (the target molecule) of the synthetic route, and then a new round of breakage on the precursor to form a new precursor is performed until all the precursor compounds are commercially available chemical raw materials. At Alfa Chemistry, we use various tree search algorithms to extend the single-step reverse synthesis to the full route design, and each step can generate thousands of precursors.

  • Reaction conditions recommendation

A complete reaction condition includes diverse details such as the quantity, quality or concentration of reactants, reaction time, and the order of addition of reagents and catalysts. We use computer-aided design tools to recommend reaction conditions to reduce the time spent on empirical screening. Our teams apply reaction conditions as variables to build a model of reaction performance for reaction optimization. Moreover, we apply machine learning method to speed up the optimization of the model and provide uncertainty evaluation through various search algorithms.

  • Forward reaction prediction

Forward reaction prediction ensures the reliability of synthetic route design by predicting reaction products. Our machine learning-based reaction prediction strategies mainly include predictions based on reaction rules and templates, graph neural networks to predict the changes of atoms and chemical bonds from reactants to products, and SMILES product prediction based on natural language processing.

Application of Our Computer-Aided Chemical Drug Synthesis

With the deepening of quantum chemistry research and the improvement of computing software, they play an important role in the process of scientific research and chemistry teaching. We apply multiple quantum chemistry computing software to provide computer-aided chemical drug synthesis services in a competitive fashion. We have prepared the most convenient services for you.

Chemists study how nature builds the molecules and identify the key biosynthetic step in planning the syntheses procedure of natural products. Alfa Chemistry uses both Quantum mechanics (QM) and molecular dynamics (MD) simulation to investigate the interactions of fundamental chemical forces in the field of complex small molecule synthesis using our professional knowledge of organic chemistry and data-based artificial intelligence (AI).

In order to provide our clients with the optimal chemical condition and pathway, Alfa Chemistry has established various methodologies to carry out reliable condition recommendation and pathway evaluation with high reliability and reproducibility by creating multiple machine-learning algorithms such as network searching, rule-based synthetic design and over-representation analysis method.

As a purely data-driven reverse synthesis methodology, retrosynthetic route planning based on molecular similarity is used to design a synthetic route from a given ideal molecule. Our teams select and use reaction templates to generate ideal precursor molecules in the similarity-based method, in which various known reactions are applied to build the model.

Nowadays, artificial intelligence (AI) technology has shown great potential in the field of drug design and synthesis. AI hase brought various opportunities for the development of the prediction of synthetic products and reaction path prediction. Alfa Chemistry has introduced advanced AI approaches to provide high quality services of synthetic products prediction and path formation studies.

The template-based computer-aided retrosynthetic route planning method is designed to generate one or more candidate precursors through the matching reaction rules in which the templates can be sorted by experts or automatically extracted from the reaction database. Our computer-aided chemical drug synthesis platform supports a diversity of machine learning (ML) methods such as deep neural network (DNN) model, Monte Carlo tree search and similarity-based approach.

Compared to the template-based computer-aided retrosynthetic route planning method, template-free automatic retrosynthetic route planning has various advantages including small mount of calculation, without requirement of a manual coding, etc. Alfa Chemistry has created a new template-free strategy for automatic reverse synthesis route planning to support your computer-aided chemical drug synthesis.

Our Advantages

  • Flexible and advanced computational methods
  • personalized and customized innovative scientific research services
  • Cost-efficient and time-saving

Our computer-aided chemical drug synthesis services remarkably reduce the cost, promote further experiments, and enhance the understanding of catalytic reactions for customers worldwide. Our personalized and all-around services will satisfy your innovative study demands. If you are interested in our services, please don't hesitate to contact us. We are glad to cooperate with you and witness your success!


  • Granda, J. M.; et al. Controlling an organic synthesis robot with machine learning to search for new reactivity. Nature. 2018, 559(7714): 377-381.

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