With the rapid development of computer algorithms, hardware, and software, drug development has greatly benefited from numerous computational methods, significantly shortening the drug discovery cycle. CD ComputaBio is committed to providing efficient and cost-effective drug analysis services to clients, accelerating the identification, design, and optimization of new drug candidates, by leveraging advanced computational biology and computational chemistry technologies.
Introduction to Computational Drug Analysis
Computational drug analysis permeates the entire drug development process. Over the past two decades, numerous computational tools have been developed and widely applied, aiming to help researchers save time and reduce costs. Currently, these tools are commonly used to identify therapeutic targets, analyze ligand-protein and protein-protein interactions, locate orthosteric and allosteric binding sites, and estimate binding free energies. From early target discovery to lead compound design and optimization, computational analysis provides powerful support for efficient and precise drug design and development.
Fig. 1 Predicted allosteric sites and their locations on SARS-CoV-2 S-protein. (Olotu F A, et al., 2020)
Tools for Drug Analysis
| Tool |
Description |
References |
| PEPPI |
A pipeline integrating structural similarity, sequence similarity, functional association data, and machine learning-based classification through a naïve Bayesian classifier model for accurate proteomic-scale prediction of protein-protein interactions. |
Bell et al. (2022) |
| ADMETlab 3.0 |
A comprehensive and efficient platform for evaluating ADMET-related parameters, physicochemical properties, and medicinal chemistry characteristics involved in the drug discovery process. |
Fu et al. (2024) |
| DrugPred |
An ensemble learning model for predicting drug targets using evolutionary scale modeling (ESM2) and amino acid composition (AAC) as features. |
Zhang et al. (2025) |
Our Services
At CD ComputaBio, we are dedicated to leveraging advanced computational biology methods to provide comprehensive drug analysis services for our clients' drug discovery and development projects, supporting the entire drug discovery and development process. Our experienced team is capable of thoroughly analyzing all aspects of drug molecules, empowering you to make more informed decisions.
Our services cover various drug molecules, including but not limited to the following categories:
- Peptide Drug Analysis
- Nucleic Acid Drug Analysis
- Protein Drug Analysis
Drug Analysis Services
Employing advanced computational methods to elucidate the 3D arrangement, conformation, and electronic properties of drug molecules, providing crucial insights for understanding their activity and interactions at a fundamental level.
- Drug Interaction Analysis
Our services investigate how drug molecules interact with target proteins and other biomolecules through technologies like molecular docking and dynamics, revealing binding modes and affinity to guide drug design.
- Physical and Chemical Properties Analysis
Predicting key properties such as solubility, lipophilicity, pKa, and stability, which are vital for assessing drugs' absorption, distribution, metabolism, and excretion within the body.
Leveraging computational models, we analyze drug efficacy, pharmacokinetic characteristics, and conduct pharmacophore mapping and QSAR studies to optimize drug activity and selectivity for desired outcomes.
CD ComputaBio utilizes sophisticated tools and databases to predict a drug's absorption, distribution, metabolism, excretion, and toxicity profiles, enabling early evaluation of potential risks and developability.
If you are interested in our drug analysis services or require further information, please do not hesitate to contact us. Our team of experts will provide you with detailed consultation and customized service plans. CD ComputaBio looks forward to collaborating with you to advance your drug development projects.
References:
- Olotu, F A.; et al. Leaving no stone unturned: Allosteric targeting of SARS-CoV-2 spike protein at putative druggable sites disrupts human angiotensin-converting enzyme interactions at the receptor binding domain[J]. Informatics in medicine unlocked. 2020, 21: 100451.
- Bell E, W.; et al. PEPPI: Whole-proteome protein-protein interaction prediction through structure and sequence similarity, functional association, and machine learning[J]. Journal of molecular biology. 2022, 434(11): 167530.
- Fu, L.; et al. ADMETlab 3.0: an updated comprehensive online ADMET prediction platform enhanced with broader coverage, improved performance, API functionality and decision support[J]. Nucleic acids research. 2024, 52(W1): W422-W431.
- Zhang, H Q.; et al. DrugPred: An ensemble learning model based on ESM2 for predicting potential druggable proteins[J]. Future Generation Computer Systems. 2025, 170: 107801.
* For Research Use Only.
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