ASX-listed biotech Algorae Pharmaceuticals (ASX: 1AI) says its new AI-driven platform aimed at identifying previously-approved drugs it can combine to create new medical treatments will be operational as soon as this year’s third quarter.
The company’s “Algorae Operating System” (AlgoraeOS) is a proprietary AI biopharmaceutical prediction platform developed in collaboration with experts from the University of New South Wales (UNSW). Management says it has steadily been building its drug database in readiness for bringing its platform online.
The system is designed to connect previously disparate data to evaluate drugs and molecules for repositioning and combining them into novel drug candidates aimed at areas of significant unmet medical need.
Algorae’s new AI platform aims to fast-track the process of finding and approving new treatments, making it quicker and more cost-effective by supporting targeted approaches to drug repositioning – which is when existing products can be used to treat a wider range of diseases.
AlgoraeOS represents the cutting edge of combination drug development in Australia.
Algorae Pharmaceuticals chairman David Hainsworth
By accessing a vast compilation of medical and scientific data, AlgoraeOS is designed to interpret a range of information sets at an enormous scale to provide predictive insights into prospective drug candidates.
Management says a critical factor is that the system has access to the most powerful supercomputer in the Southern Hemisphere. Dubbed “Gadi” and operated by Australia’s preeminent high-performance data, storage and computing facility, National Computational Infrastructure, the supercomputer has previously been used for climate modelling and natural disaster prediction.
According to UNSW, the current pre-clinical stage of drug discovery can take up to six years, potentially costing billions of dollars.
But Algorae says AI tools are helping speed up the development process by repositioning drugs, identifying drug interactions, assessing toxicity and predicting novel drug targets. It adds that machine learning, deep learning and neural network models can predict drug interactions, assess toxicity and optimise compound designs, guiding researchers towards more promising avenues of investigation while saving significant time and resources.
Data acquisition and customisation to build on the AlgoraeOS database is currently underway, with a focus on four key pillars of information, including drug, cellular and biological data, in addition to chemistry. Initial acquired data already included in the company’s library cover scientific and medical fields including drug chemical structure, drug-target interactions, gene expression and drug perturbation.
We are delighted with the advancements in our artificial intelligence platform. AlgoraeOS represents the cutting edge of combination drug development in Australia.
Algorae Pharmaceuticals chairman David Hainsworth
The company entered into a memorandum of understanding with UNSW in September last year to build upon a sophisticated AI model trained for pharmaceutical prediction that had already been developed by data specialists within the university’s Data Science Hub. The project is led by Associate Professor Fatemeh Vafee, who attained a PhD in Artificial Intelligence from the School of Computer Science at the University of Illinois in Chicago.
Since its collaboration with UNSW, Algorae has appointed three full-time development staff, with two supported by funding from the CSIRO Next Generation AI Graduate Program. Under the program, the CSIRO provides about two-thirds of the funding required for the development staff, while Algorae provides a third of the costs for a three-year term.
A fourth full-time member, also eligible for CSIRO grant funding, is expected to join the team shortly as multiple candidates with sophisticated pharmacology experience are being considered for the role.
With Algorae making significant progress with its AI platform, in addition to the backing of UNSW and the Gadi supercomputer, the market will be keeping a close eye on what the company can deliver once its system is fully operational.
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