AI will revolutionize drug discovery

In health care, two exciting uses of artificial intelligence — in the clinic for patient care and in the laboratory for drug discovery — are remarkably different applications. That perhaps explains why, though it’s still early days for both, they are developing at different rates.

Drug discovery is the preliminary step in the process of a novel drug identification and its therapeutic target. Artificial intelligence (AI) is commonly used in the healthcare industry for drug discovery. Artificial intelligence technology has the ability to recognize drug targets, and play a significant role in drug design, discovery, identification and screening of molecules instantly and effectively. Drug discovery or new drug target are being estimated based on potency, bioavailability, efficacy, and toxicity.

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In the clinical setting, AI works with known parameters, typically running through a classification process based on experiences of what works and what doesn’t for different types of patients. The potential of AI here is significant, and the early successes are truly exciting.

The opportunity is equally compelling in drug discovery, particularly in areas of high unmet need such as rare and hard-to-treat cancers and neurodegenerative conditions. Artificial intelligence can ingest and reason over information from the scientific literature and databases, as well as patient-level data, to identify potential approaches to treat diseases by proposing a drug target, designing a molecule, and defining patients in which to test that molecule to drive greater clinical success.

But the questions being asked of artificial intelligence in this sphere are fundamentally different than they are in clinical applications. AI is in uncharted territory here, searching for the novel, not the known. In any setting, AI requires training with positive — and ideally some negative — examples. This is a particular challenge in predicting new targets for glioblastoma, Parkinson’s, and other conditions for which no treatment has yet been shown to reverse the disease course.

The potential of AI in drug discovery

The pharmaceutical industry is facing a crisis is R&D. About 50% of late-stage clinical trials fail due to ineffective drug targets, resulting in only 15% of drugs advancing from Phase 2 to approval. And researchers tend to coalesce around the same disease areas and targets.

Artificial intelligence can help expand the drug discovery universe by making predictions in more novel areas of biology and chemistry. By extracting text from scientific papers, AI can help identify relevant information faster and make links between biomedical entities, such as medicines and proteins, often with relatively little information.

In amyotrophic lateral sclerosis (ALS), for example, 50 clinical trials in the last two decades have failed to show any positive results, leaving only two approved drugs on the market that have shown only modest benefits to patients. This is an area crying out for new approaches.

Followings are the companies operating in this research:

• Atomwise, Inc.
• Insilico Medicine
• BIOAGE
• Numerate
• NuMedii, Inc.
• Envisagenics
• Cloud Pharmaceuticals, Inc.
• BenevolentAI
• twoXAR, Incorporated
• Exscientia

Source: statnews & theinsightpartners