AI Race in Drug Discovery Intensifies as Insilico Medicine Brings AI-generated Molecule To First-in-human Trial
Getting a step closer to treating Idiopathic Pulmonary Fibrosis
A Hong Kong-based company applying a state-of-the-art artificial intelligence (AI) platform for drug discovery, Insilico Medicine, has announced the start of a first-in-human microdose trial of ISM001-055 — a potentially first-in-class small molecule inhibitor of a novel biological target developed by the company for the treatment of idiopathic pulmonary fibrosis (IPF). This news may become a ray of hope for an increasing number of people, primarily the elderly, diagnosed with IPF -- an irreversible progressive disease with median survival time ranging from 2.5 to 3.5 years after the first diagnosis if untreated, with few cures currently available on the market.
Insilico Medicine, a company earlier highlighted in BiopharmaTrend’s yearly review “R&D Trends To Follow In Pharmaceutical Industry In 2021 And Beyond'', reported in a press release that the first healthy volunteer in an Australian study has received a limited, intravenous dose of ISM001-055. This small molecule showed promising efficacy for IPF and a good safety profile that led to its nomination as a preclinical drug candidate in December 2020 for IND-enabling studies. The molecule has been de-novo designed using Chemistry42, a subsystem of the company’s Artificial Intelligence-based end-to-end R&D platform Pharma.AI.
The molecule ISM001-055 is an inhibitor for a novel pan-fibrotic target earlier also discovered by Insilico Medicine using PandaOmics -- a target discovery subsystem of Pharma.AI, connected with Chemistry42 in an integrated workflow. Pharma.AI also includes its third major subsystem -- InClinico, which can predict clinical trials success rates, recognize the weak points in trial design, while adopting the best practices in the industry.
Notably, Insilico Medicine completed the entire discovery process from target discovery to preclinical candidate nomination within 18 months on a budget of $2.6 million, demonstrating a record-setting performance of its R&D platform. “We believe this is a significant milestone in the history of AI-powered drug discovery because to our knowledge the drug candidate is the first-ever AI-discovered novel molecule based on an AI-discovered novel target. We have leveraged our end-to-end AI-powered drug discovery platform, including the usage of generative biology and generative chemistry, to discover novel biological targets and generate novel molecules with drug-like properties. ISM001-055 is the first such compound to enter the clinic, and we expect more to come in the near future," Insilico Medicine’s Chief Scientific Officer, Feng Ren, Ph.D., said in a statement.
Blazing the trail to AI-enabled drug discovery model
Insilico Medicine was founded in 2014 with the idea of adopting deep learning to solve the challenges of pharmaceutical research. Those were the times of a global rise of deep learning, particularly in the area of image recognition and video processing -- ignited by a wave of successes in theoretical and practical machine learning. For instance, in 2012 a convolutional neural architecture AlexNet scored a solid win in the ImageNet competition, demonstrating the outstanding ability of deep learned neural nets to work with images. The same year, Google announced its AI system managed to spontaneously identify a cat in a series of youtube videos -- via unsupervised learning.
Generative Adversarial Networks (GANs)
The GAN model architecture includes two sub-models: a generator model for designing new examples and a discriminator model (adversary) which tries to classify if generated examples are real (from training dataset), or fake -- generated by the generator model. The two models compete against each other until GAN excels at generating examples non-distinguishable from training data, and belonging to the same distribution as the training data.
In 2014 Ian Goodfellow introduced an innovative deep learning architecture -- generative adversarial networks (GANs) as a general concept to solve de-novo design problems in various fields. Just a couple of years later, Insilico Medicine pioneered a successful application of GANs to specific problems in drug discovery, such as de-novo molecular design.
The team at Insilico developed several improvements and new features to their GAN-based AI platform for drug design and started patenting findings. In 2017 they built multiple working GAN models, including druGAN for fingerprints, ORGAN for SMILES, various recurrent neural networks (RNN) architectures with reinforcement learning and LSTM, agile temporal convolutional networks (ACTNs), and a reinforced adversarial neural computer (RANC).
“We pioneered this particular field of AI in drug discovery but I think that the application of the Generative Adversarial Networks (GANs) and combinations of GANs with Reinforcement Learning (RL) to the generation of novel molecular structures with the desired properties was the real breakthrough”, commented Dr. Alex Zhavoronkov for back in 2017.
Continue reading
This content available exclusively for BPT Mebmers
We use cookies to personalise content and to analyse our traffic.
You consent to our cookies if you continue to use our website. Read more details in our
cookies policy.
