Traditional drug discovery and development processes are often time-consuming, expensive, and prone to high rates of failure. At CD ComputaBio, we are at the forefront of innovation in the pharmaceutical industry, leveraging cutting-edge artificial intelligence technologies to transform the landscape of drug development. With a dedicated team of experts and state-of-the-art computational tools, we are committed to accelerating the discovery and development of life-saving medicines. Our AI-driven approach enables us to decode complex biological processes, predict drug-target interactions, and optimize lead compounds with unprecedented speed and precision.

Our Services

Our team can perform fast and accurate in silico screening of drug candidate compounds, target profiling, compound structure scoring in de novo design, and other related calculations.

Fig 1: AI-aided drug development

De Novo design is one of the most widely used drug design methods to discover new potentially active compounds, which can save researchers a lot of time.

CD ComputaBio has abundant database resources, high-performance computer servers, and can provide professional molecular docking and virtual screening services.

Fig 2: AI-aided drug development

CD ComputaBio currently has molecular dynamics software such as AMBER, GROMACS and NAMD.

CD ComputaBio provides a new solution to solve the generalization issue of deep learning-based docking system for virtual screening.

CD ComputaBio has formed a team of experts excellent in PK/PD modeling, providing AI-driven solutions according to your detailed requirements.

Through the integration of machine learning, deep learning, quantum simulation, and high-throughput experimentation, our experts enable formulation scientists to rapidly, comprehensively and intelligently develop clinically differentiable products.

Our Analysis Methods

Advanced Machine Learning Models

Our proprietary machine learning models are trained on diverse datasets encompassing genomics, proteomics, chemical structures, and clinical outcomes to meet special needs.

Virtual Screening

Our AI algorithms sift through vast chemical space, identifying molecules with the highest likelihood of binding to target proteins, thus expediting the lead optimization process and minimizing experimental trial and error.

Molecular Docking Simulations

By simulating the interactions at the atomic level, we optimize the chemical structures of lead compounds for enhanced potency and specificity, paving the way for the design of more effective therapies.

Pharmacophore Modeling

Our pharmacophore modeling tools help us understand the essential structural and chemical features required for a drug molecule to bind to its target.

Service Highlights

Fig 3: AI-aided drug development

Target Identification and Validation

Our AI algorithms identify novel drug targets based on biological datasets and mechanistic insights.

Fig 4: AI-aided drug development

Drug Repurposing

Through computational analysis of existing drugs and their interactions with biological targets, we identify opportunities for drug repurposing.

Fig 5: AI-aided drug development

Lead Optimization

By iteratively refining chemical structures and predicting pharmacokinetic properties, we accelerate the transition of leads into viable drug candidates.

Fig 6: AI-aided drug development

ADME-Tox Profiling

Our AI platforms assess the absorption, distribution, metabolism, excretion, and toxicity (ADME-Tox) properties of drug candidates, enabling early identification of potential safety issues.

At CD ComputaBio, we are dedicated to pushing the boundaries of drug development through the innovative application of artificial intelligence. Our commitment to excellence, scientific rigor, and customer satisfaction sets us apart as a trusted partner in accelerating the discovery of transformative therapies. If you are interested in our services or have any questions, please feel free to contact us.


  • Chen W, Liu X, Zhang S, et al. Artificial intelligence for drug discovery: Resources, methods, and applications[J]. Molecular Therapy-Nucleic Acids, 2023, 31: 691-702.


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