With the rapid development of computer technology, the application of computational biology and computational chemistry methods in drug discovery and design has become a core topic in modern molecular biology and medicinal chemistry. CD ComputaBio utilizes advanced computational methods and artificial intelligence technology to provide clients with professional drug discovery services, facilitating the emergence of new drugs for pharmaceutical companies and research institutions.
Introduction to Drug Discovery
Drug discovery is the process of finding new drugs to combat diseases, requiring diverse technologies and specialized knowledge. It is estimated that it takes an average of $2.8 billion and 15 years for a drug to go from discovery to successful development. Traditional experimental methods are inefficient and costly, posing significant challenges to drug discovery. The advent of high-performance computing hardware and the acquisition of multi-omics data have enabled computational methods and artificial intelligence technologies to move from theory to practical application, offering the potential for more efficient and cost-effective solutions in drug discovery.
Fig. 1 Framework of AI technique application to drug discovery and evaluation. (Chen W, et al., 2023)
Challenges in Drug Discovery
Drug discovery faces multiple challenges. Commercially, despite a high number of new approvals, the potential for blockbuster drugs is limited, partly due to a focus on small indications and competition from existing generic drugs; orphan drugs, while incentivized, have restricted market potential. In terms of clinical translation, Phase II trials exhibit high attrition rates, primarily due to insufficient efficacy and safety concerns, with discrepancies between animal models and human physiology, as well as a lack of effective biomarkers, posing significant obstacles. Furthermore, a substantial portion of potential drug targets remains unexplored, and while novel drug modalities such as biologics can target previously undruggable targets, they often come with higher production costs.
Fig. 2 Drug approvals and R&D spendings over the last decades. (Tautermann C S, et al., 2020)
Our Services
Leveraging computational technologies, CD ComputaBio assists in the rapid screening of compounds with therapeutic effects, significantly shortening the drug discovery process and providing strong support for pharmaceutical research and development. Our services cover multiple drug discovery areas, including but not limited to:
Small Molecule Drug Discovery
CD ComputaBio rapidly identifies lead compounds targeting the active sites of proteins through virtual screening, molecular docking, and pharmacophore modeling. We optimize ADMET properties to accelerate the clinical translation of small molecule drugs for anti-cancer and anti-infection therapies.
Peptide Drug Discovery
Leveraging deep learning and conformational prediction algorithms, we design highly stable and selective peptide molecules that target complex interfaces such as GPCRs and ion channels, overcoming the limitations of traditional small molecules in binding to certain targets.
PROTAC Drug Discovery
CD ComputaBio utilizes computational tools to simulate the ternary complex structure of E3 ligase-target protein-PROTAC. We optimize molecular linkers and ligand combinations to degrade "undruggable" targets, providing novel strategies for cancer and neurological diseases.
Nucleic Acid Drug Discovery
Through sequence design and chemical modification prediction, we develop innovative siRNA, ASO, and mRNA therapies. We optimize their stability and significantly enhance the targeting of delivery systems to tackle genetic diseases, viral infections, and other gene-level disorders.
Antibody Drug Discovery
CD ComputaBio combines structural biology and AI epitope prediction to design high-affinity antibodies. We simulate antibody-antigen dynamic interactions to optimize humanization and affinity maturation, accelerating the development of cancer immunotherapies.
Protein Drug Discovery
Based on computational protein design and molecular dynamics simulations, we modify natural proteins or de novo design functional protein drugs to modulate protein-protein interactions, developing novel targeted therapies to address unmet medical needs in areas such as cancer and autoimmune diseases.
Computational Methods for Drug Discovery
Traditional experimental methods in drug discovery face challenges such as time-consuming processes, high costs, and low efficiency. CD ComputaBio combines computational methods like artificial intelligence (AI) and computer-aided drug discovery to provide clients with efficient drug discovery services.
- Computer-aided Drug Discovery
Computer-aided drug discovery utilizes computer technology, combining methods like molecular docking and molecular dynamics simulations, to perform virtual screening on large-scale chemical libraries, efficiently identifying potential active drug molecules. We are able to rapidly identify promising drug molecules, providing strong support for new drug development.
Artificial intelligence (AI) holds the potential to revolutionize drug discovery and early drug development by addressing the inefficiencies inherent in traditional methods. Tools like AlphaFold precisely predict protein structures, aiding in drug development. AI also facilitates virtual screening and de novo drug design, enabling the creation and optimization of molecular structures.
CD ComputaBio is a trailblazer in the field of drug discovery, leveraging our extensive expertise in artificial intelligence and computer-aided drug design to offer comprehensive drug discovery services to researchers. If you are interested in our services or have any questions, please feel free to contact us.
References:
- Chen, W.; et al. Artificial intelligence for drug discovery: Resources, methods, and applications[J]. Molecular therapy Nucleic acids. 2023, 31: 691-702.
- Tautermann, C S. Current and future challenges in modern drug discovery[J]. Quantum mechanics in drug discovery. 2020: 1-17.
* For Research Use Only.
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