Small Molecule Design and Optimization

Creative Biolabs has ample experience in AI (artificial intelligence)-based methods applied in the drug design and discovery field. Currently, we have developed a wide variety of assays for small molecule design and optimization to promote its affinity, specificity and validity, mainly ranging from in silico molecule screening, molecular modeling, to AI-based molecule optimization.

Introduction of Small Molecules in Drug Discovery

Small molecules, a number of low molecular weight compounds, have been considered as perfect targets for designing new drugs in human disease therapy. The screened small molecules are tested in different types of cells, animal models, and eventually clinical trials. However, this normal drug discovery process can be time-consuming and least cost-effective. Recent studies have revealed that computer-based small molecule design tools have shown promising results in the process of small molecule design and optimization. Among them, AI-aided platform plays an important role in guiding the construction of small molecules that are of most relevance to targeted drugs.

Our Services for Small Molecule Design and Optimization

At Creative Biolabs, we are dedicated to helping our worldwide customers to select, design, and optimize the small-molecule for drug discovery.

Small molecule design and optimization. Fig.1 Small molecule design and optimization.

For instance, deep learning (DL) methods are commonly used for achieving target recognition of small molecules in drug discovery. These DL models can successfully predict the activity and property of various small molecules. As one of the most popular DL architectures, a convolutional neural network (CNN) has been developed by analyzing the sparse connectivity and shared weights to mimic the interaction between candidate compounds and proteins. Besides, a big data library for training the DL models, including the AlphaGo, has been established to collect enough data for AI-aided intricate simulation.

Structure of convolutional layer and architecture of convolutional neural network (LeNet-5). Fig.2 Structure of convolutional layer and architecture of convolutional neural network (LeNet-5). (Jing, 2018)

Creative Biolabs has developed a novel AI-based small molecule design and optimization platform to boost the functions of our AI-driven drug discovery pipeline. Our ligand- and/or structure-based databases and focused library approaches will remove the obstacles of your projects and bring breakthroughs to affect the future of small-molecule drug discovery. If you are interested in our services, please feel free to contact us or send us an inquiry.


  1. Jing, Y.; et al. Deep Learning for Drug Design: an Artificial Intelligence Paradigm for Drug Discovery in the Big Data Era. Chem Soc Rev. 2018, 20(4): 79.
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