AI-Powered

AI-Driven Drug Discovery and Development
Enhancing the Speed and Economics of Drug Development Lifecycle

AI is changing the game in pharmaceutical research and development, offering unmatched speed, high success rate, and cost efficiency in drug discovery and development.

AI-derived molecules have a success rate of 80-90% in Phase I trials.

Challenges of Traditional Drug Discovery Techniques

The average time taken by a new drug molecule to proceed from lab to market, in a typical scenario, is estimated as 10–15 years.
Despite investing a lot of time and money into discovering and launching new drugs, many pharma players report low success rates. Traditional approaches to drug discovery and development face considerable challenges:

Conventional methods primarily rely on identifying and improving existing compounds.

Limited understanding of complex biological systems can slow development pipelines.

Incomplete or heterogeneous data hinder accurate predictions, increasing the risk of failure.

Poor success rate due to a small percentage of drug candidates moving from lab to market.

Exhaustive, costly, and inefficient trial designs leading to substandard patient outcomes.

Difficulties in identifying right biological targets and comprehending disease mechanisms.

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The Solution:
AI-Enabled Drug Discovery and Development

AI expedites drug discovery and development procedures without overspending. Using deep learning, machine learning, and other advanced techniques, AI helps reduce the time taken by a new drug compound to travel from lab to market.

Machine learning algorithms to identify and validate potential drug targets.

Design and optimize new drug candidates with greater safety and efficacy.

AI for drug screening, repurposing and predicting drug-patient interactions.

How AI Helps Design Clinical Trials

Identify potential drug candidates and targets faster using biological data.
Fast and safe virtual screening of billions of drug compounds.
Choose patients, forecast outcomes, and adapt clinical trial designs.

AI Drug Discovery in Clinical Trials

success rate in phase I trials
80 - 0 %
compound annual growth rate
0 %
reduction in workplace injuries
0 %

AI-Accelerated Drug Discovery

years average timeframe for drug discovery
1 - 0
reduction in the length of trials
15 - 0 %

Benefits of AI-Enabled Drug Discovery and Development

Identify potential drug targets faster by analyzing extensive datasets.

Design new and effective molecules with enhanced pharmacological traits.

Optimize clinical trial designs by identifying appropriate patient subgroups.

Accurate predictions of the bioactivity of different drugs and compounds.

Discover promising therapeutic uses for existing drugs and medications.

Tailor drug combinations and treatment approaches for individual patients.

AI automated inspections to ensure drugs meet strict quality standards.

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How Fingent Helps With AI-Powered Drug Discovery and Development?

Being a leader in custom AI software development, we help our clients launch cost-effective and efficient healthcare tech solutions that catalyze significant advances in the development of life-saving drugs and personalized treatments.

Custom AI software development for life sciences and biopharma

Healthcare AI consulting services and software solutions

AI-powered custom dashboards and visualizations for R&D teams

AI-enhanced predictive technology and knowledge graphs

Customized software solutions for multi-modal data modeling

Enterprise AI applications tailored to run drug discovery workflows

Custom drug discovery platforms employing Gen AI and AutoML

Key Takeaways

AI transforms drug discovery and development:
Discover, verify, and prioritize potential biological targets with greater speed and accuracy.
Rapid screening of extensive compound databases to identify potential lead molecules.
Forecast compound toxicity in preclinical examinations, before human testing.
Increase clinical trial success by enabling risk-based monitoring and treatment adherence.

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