AI for Drug Discovery
Streamlining drug discovery through AI
by identifying drug targets,
accelerating the testing and validation
process, predicting drug efficacy and
toxicity
MODEL ACCURACY
Accuracy of upto 95% in new drug molecule discovery and feasibility
COST REDUCTION
25%-30% reduction in costs for preclinical and clinical development
INCREASE IN EFFICIENCY
Reduction in time spent for new molecule screening
FASTER TIME TO REVENUEFaster time to market and revenue
realization
Business Challenge
It is estimated that close to $2.6 Billion
is spent in new drug discovery starting from
discovery of new drug molecules, to
development, to clinical trials and FDA
approval. Most of this cost is owing to the
complexity of molecule screening process,
high development costs due to increasing
input costs, levels of complex clinical
trials to be conducted.
While these are direct costs, there are
hidden indirect cost to company such as
lengthy time to market anywhere close to ten
years for a new drug, lower time to revenue,
all of which leads to prolonged sales cycle
and therefore are indirect costs. Data
science and AI has made significant strides
in simplifying and automating the process of
drug discovery and clinical trials.
Our AI drug discovery use cases are in the following areas
- AI based virtual screening of molecules
- AI enabled de-novo drug design
-
AI based drug combination
optimization
- Pre clinical research
- Drug review
- Drug safety monitoring
-
AI for Drug repurposing to predict
accurate drug-target interactions
Our AI Models for all stages of drug discovery
- Traditional reinforcement learning
- Generative Adversarial Networks (GANs)
- Quantum Machine Learning (QML)
- Molecular Docking Algorithms
- Deep learning based drug-target interactions
- Neural networks to screens millions of compounds against set targets
The benefits of AI enabled drug discovery
- AI models can show accuracy of upto 95% in new molecule feasibility
- Significant reduction in time for screening and molecule selection
- 25%-30% reduction in costs for preclinical and clinical development
- Accurate monitoring of drug interaction with target
- Increased number of approved therapies and faster time to market for new drugs
-
Lower time to revenue for drug
companies