Drug discovery service
PREDIT: Our Proprietary AI-based In-silico Platform
Drug discovery is a complex and expensive process that can take many years. However, the use of computational drug design has emerged as a powerful tool to accelerate drug discovery and development. As experts in this field, we believe that close collaboration with the pharmaceutical industry can help to bridge the gap between the development of new computational methods and their application to real-world drug discovery projects. By leveraging our expertise, we can work with pharmaceutical companies to advance drug discovery programs and reduce the time and cost associated with traditional drug discovery approaches.
Limitations of the traditional drug discovery pipeline
-
On average, it takes 10-15 years and costs $2.6 billion to develop one new medicine, including the cost of the many failures
-
Only 12% of new molecular entities that enter clinical trials eventually receive U.S. Food and Drug Administration (FDA) approval
-
Approximately 7,000 rare diseases exist today yet only 5% have an available treatment
-
Any tactic that may help to reduce the significant costs associated in the initial stages of drug discovery is expected to significantly improve the productivity of drug discovery efforts
-
Therefore, for the sake of time, effort, and cost, computer-aided drug design is crucial in the contemporary drug development pipeline; starting from target identification to lead optimization
Our capabilities
-
Team of experts with extensive experience in the development and implementation of computational drug design and discovery methods
-
Expertise in the use of state-of-the-art technologies and methodologies to design and optimize drug candidates
-
Expertise in molecular modeling, virtual screening, structure-based drug design, ligand-based drug design, and machine learning algorithms
-
Access to high-performance computing resources and cutting-edge software tools that enable us to perform large-scale virtual screening, molecular dynamics simulations, and other computational studies
-
PREDIT – PHARMAECONOMICA AI based In-silico Platform contains our proprietary in-house libraries and discovery algorithms for in silico screening. It includes a carefully curated sub library of 50 million drug- like molecules (good ADMET, and PAINS free) based on a ~4 Billion compound library
-
Our proprietary platform is also able to identify high-affinity cyclic peptides for any target and modify them with unnatural amino acids using a semi-automated process
Our services
-
Providing expertise in computational drug design and discovery to aid in the selection and optimization of drug candidates
-
Conducting virtual screening studies to identify novel drug candidates that are most likely to have the desired pharmacological properties
-
Developing predictive models to estimate the efficacy and safety of drug candidates
-
Optimizing the PKPD properties of drug candidates by using molecular modeling and simulation techniques to help design drugs with better efficacy, safety, and pharmacokinetic profiles
-
Provide insights into potential drug targets and mechanisms of action helping pharmaceutical companies design drugs that target specific proteins or pathways
-
Contributing to the design and interpretation of preclinical and clinical studies
Your benefits
-
Identify the most promising drug candidates at an early stage
-
Identify novel drug candidates that are likely to have the desired pharmacological properties in a faster time and in a cost-effective manner
-
Prioritize drug candidates for further development based on reliable predictive models using machine learning algorithms that can estimate the efficacy and safety of drug candidates with a high probability of success
-
Design of drugs with better efficacy, safety, and pharmacokinetic profiles
-
Design drugs that target specific proteins or pathways
-
Design more informative and efficient preclinical and clinical studies
-
Integrate a cost-effective, time-saving, fast, and automated process to your drug discovery activities
-
Get actionable insights into the drug-receptor interaction pattern
-
Minimize synthetic and biological testing efforts
-
Minimize the possibility of failure in the final stage
