Pepticom set to develop AI models enabling peptide-based drug discovery

By Vassia Barba

- Last updated on GMT

(Image: Getty/selvanegra)
(Image: Getty/selvanegra)

Related tags Artificial intelligence Pepticom Peptides Peptide

Pepticom’s technology aims to ‘vastly reduce’ risk of failure in peptide drug candidates’ discovery and development, by utilizing artificial intelligence models.

The Israeli company announced the closing of its Series A funding at $5m (€4.49m) from the Chartered Group investment firm, which is expected to use the funds to enable further development of artificial intelligence (AI) models that can be used in peptide drug discovery.

According to the company, the pharmaceutical industry has recently shown an increased interest in peptide R&D, leading to a resurgence of peptide drug candidates for various indications, since the molecules can be ‘highly selective and efficacious, as well as relatively safe’.

The AI technology developed by Pepticom aims to streamline and accelerate researcher’s discovery of peptide-based drug candidates, compared to traditional methods which are ‘costly and time consuming’, the company stated.

Discovery is achieved “by searching an enormous set of possible solutions, vastly reducing the risk of failure during development.”

More specifically, the technology covers a chemical space of 1,030 possible molecular options, which is larger than current screening techniques.

Simultaneously, the platform filters out the most suitable candidates, separating them according to their salient properties, including solubility and permeability.

“The ability to search a large amount of variables while considering their pharmacological impact, and also eliminating non-viable molecules at an early stage is groundbreaking in peptide drug discovery”,​ the company stated, adding that its solution has the potential to bring down the cost of drug discovery.

Immanuel Lerner, Pepticom’s CEO, commented, “[The technology] already improves discovery time by almost a year, and this investment will further reduce that time and thus improve time to market considerably,"​ Lerner concluded.

Related topics Preclinical Research Preclinical

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