Virtual Screening Service
CD ComputaBio is at the forefront of this revolution, offering state-of-the-art AI-aided virtual screening services that streamline drug discovery processes, enhance efficiency, and accelerate the development of novel therapeutics. With a deep commitment to innovation and scientific excellence, CD ComputaBio combines expertise in computational biology, AI algorithms, and virtual screening techniques to provide unparalleled services to pharmaceutical companies, research institutions, and biotech firms worldwide.
Applications of Virtual Screening Service
Drug Repurposing
By repurposing existing drugs for new indications, virtual screening enables the exploration of alternative therapeutic avenues efficiently.
Polypharmacology Studies
Virtual screening allows for the exploration of compounds that can interact with multiple targets, offering insights into polypharmacology and drug combination therapies.
Toxicity Prediction
Predicting potential adverse effects of compounds early in the drug discovery process aids in selecting safer and more potent candidates for further study.
Our Services
Ligand-Based Virtual Screening
We curate and preprocess extensive ligand libraries comprising small molecules, natural compounds, or approved drugs to enhance the diversity and quality of compounds screened during the virtual screening process.
Structure-Based Virtual Screening
Our SBVS service utilizes sophisticated AI and machine learning algorithms to screen vast libraries of compounds against well-characterized biological targets. By analyzing the three-dimensional structure of target proteins and virtual compounds in silico, we can predict the binding affinity.
Harnessing the power of artificial intelligence, our service integrates machine learning algorithms and deep learning models to enhance the predictive accuracy of virtual screening results. AI-driven analyses offer valuable insights into structure-activity relationships and assist in predicting potential off-target effects.
CD ComputaBio conducts precise 3D-QSAR modeling and molecular docking simulations to predict the binding modes of small molecules with target proteins, enabling the identification of potential drug candidates based on structural complementarity.
Analysis Methods
- Machine Learning for Hit Prediction
CD ComputaBio harnesses machine learning algorithms to analyze molecular datasets, predict potential hits, and prioritize lead compounds with the highest probability of biological activity against target proteins. - Deep Learning in Virtual Screening
Through the integration of deep neural networks, we enhance the accuracy of virtual screening predictions by capturing complex ligand-target interactions, recognizing subtle structural patterns, and optimizing lead optimization strategies. - Structure Activity Relationship Analysis and Development
Structure-activity relationships (SARs) and quantitative structure–activity relationships (QSARs) are basic and theoretical models to drug research and development, which can be used to predict the physicochemical, biological, and other properties of chemicals, and guide compound optimization. - Lead Drug Screening, Scoring, and Ranking
A scoring function is utilized in docking to approximate the free energy of binding between the protein and the ligand in each docking pose.
Our Capabilities
Molecular Docking
Secondary Structure Analysis
Backbone Hbond Monitoring
At CD ComputaBio, we are committed to pushing the boundaries of drug discovery through our AI-aided virtual screening service. By integrating advanced AI technologies with expert bioinformatics analysis, we offer a comprehensive solution that accelerates the identification of lead compounds, optimizes drug discovery workflows, and enhances the success rate of pharmaceutical research projects. If you are interested in our services or have any questions, please feel free to contact us.
References:
- Zhang Y, Luo M, Wu P, et al. Application of computational biology and artificial intelligence in drug design[J]. International journal of molecular sciences, 2022, 23(21): 13568.
- Jang W D, Kim G B, Kim Y, et al. Applications of artificial intelligence to enzyme and pathway design for metabolic engineering[J]. Current Opinion in Biotechnology, 2022, 73: 101-107.
Services