LONDON, Oct 29 (Reuters) - Biotech firm Iambic

Therapeutics unveiled on Tuesday what it says is a breakthrough

artificial intelligence model that could drastically reduce the

time and money needed to develop new drugs.

A growing number of tech startups are using AI to advance

pharmaceutical research. Iambic, which has previously won

investment from tech giant Nvidia ( NVDA ), published details of

its new AI drug discovery model, named "Enchant".

Enchant was trained on large troves of pre-clinical data,

derived from laboratory tests conducted on drugs before they

were ever tested on humans. The model has been designed to

predict how a given drug will perform at the earliest stage of

development.

In a white paper published by Iambic, Enchant showed a high

degree of accuracy when predicting how well the human body would

absorb certain drugs, with results cross-referenced to

real-world outcomes.

The company said its model set a new benchmark, with a 0.74

accuracy prediction score. By comparison, earlier models had

only achieved as high as 0.58.

Iambic co-founder and chief technology officer Fred Manby

told Reuters that researchers using Enchant could potentially

halve the investment needed to develop some pharmaceuticals, as

they could see how successful a drug is likely to be at the

earliest stage.

"The cost of getting a product to market is often quoted at

around $2 billion, and a lot of that isn't about the programme

costs, but the failure rates. The costs of getting a product all

the way to a marketed medicine derive from a high chance of

late-stage failure," he said.

"If you make a 10% improvement in each stage of clinical

development, you would basically halve the cost, because it

applies cumulatively."

Frances Arnold, who won the chemistry Nobel Prize in 2018

and sits on Iambic's board, told Reuters the development

represented a major advance in the use of AI for drug discovery.

Citing Google DeepMind's AlphaFold program, which

recently won its developers the chemistry Nobel Prize, Arnold

said Enchant addressed a different challenge in the drug

discovery pipeline.

"AlphaFold predicts the 3D structure of how a molecule binds

to a protein target, but structure is not enough," she said.

"The success of a drug candidate is determined by its

pharmacokinetic, efficacy, and toxicity properties. Enchant

addresses these distinct and important challenges."