Although there are higher philosophical goals to generating an advanced artificial intelligence, most of the modern applications are focused on problem-solving and learning to refine predictive models. A lot of the principles in my computer science classes involved information recall, sorting, and storage, and as computers and programming languages evolved, so too did the strategies to leverage that increased computing power. For example, you may have heard terms like “neural network” and “machine learning,” which refers to the modeling strategies employed to organize and decipher huge datasets similarly to how neurons in complex nervous systems communicate with each other. The goal of these computational strategies is to improve predictive algorithms that are trained on prior experimental data in order to drive new research directions in a manner that is far more efficient than prior “shotgun” type approaches. In focusing research directions, the AI and other computational helpers can reduce the resources used in terms of time, personnel, and reagents.
In addition to better algorithms to target content to users on social media and sites such as YouTube, AI is heavily leveraged in modern times to drive scientific discovery. In particular, AI has changed the landscape of biomedical research, mostly for the better. There are various logistical applications for AI, which includes supply chain optimization and drug pricing, but for the bench and computational scientists, the motivation for using AI is in the potent predictive algorithms that drive drug and biomarker discovery. 1 For example, machine learning was employed to discover a new antibiotic against a notoriously resistant bacterial pathogen. By training a neural network with experimental data comprising molecules that were found to inhibit the growth of the superbug Acinetobacter baumannii, scientists were able to discover abaucin, a structurally new molecule with effective performance against the pathogen that was derived from the AI-driven analysis. 2 There are many other applications of AI, including some that I didn’t even know were subjects of interest, such as using the brain’s ability to remember faces to improve facial recognition systems, enhancing our understanding of gene activation and infectious diseases, and even a program that can accurately identify species in the wild. The only limit thus far seems to be the imagination of the programmer, but once the algorithm is released to do its stated function, the discoveries can be quite impactful.
Every year, it seems like dozens of new biotech startups emerge with an exciting new approach to improving the human condition, and others explode into the limelight as they develop their big breakthrough. Some of them persist, and some are absorbed into existing big pharma companies, but the fact is that these companies can each acquire hundreds of millions of dollars in funding, leading to billions in revenue and profit. Among the perceived best companies of 2024 is Cellarity, which uses proprietary AI models to develop novel drug candidates across a variety of diseases.
It may not be only the usual suspects in biopharma getting into the act, as ByteDance, the parent company of the popular social media platform TikTok, is starting to recruit talent in computational biology and other disciplines to set up AI teams for drug design and science. Although it isn’t totally clear why a social media company is getting involved in AI-driven bioscience, experts suggest that TikTok data could be used for large-scale hypothesis generation based on the content produced by its many users, especially those who are interested in health, science, and well-being focused content.
Because AI models are reliant on their training datasets, the availability of accurate and vetted information via those datasets is extremely important. Also, the accuracy of predictive models is sometimes in question, and even the best models must be validated through experimentation and clinical testing. 1
According to at least one forecast, business spending on AI is expected to hit $110 billion globally in 2024, with many companies already adopting multiple AI systems to leverage this growing technology. And because AI is trained on datasets, there will be discussions on how and which data can be accessed, pursuant to privacy and confidentiality laws. There are societal ramifications in the increased usage of AI, similarly to the trepidation when industries began to automate many tasks, but the positive outlook is that by automating certain tasks or analyses, we can focus human talents towards other aspects of our society.
Overall, AI, as with many other scientific innovations, has strong potential to change humanity for the better if used wisely and responsibly. With mindful regulation and personal vigilance, AI can continue to benefit science and society without threatening our way of life through invasion of privacy and various biases generated by its modeling.
Dr. Hao Cheng, PhD, of Yurogen joins BioChat to discuss the many considerations that go into engineering a more effective antibody reagent for research and therapeutic use. Dr. Cheng and Yurogen leverage the power of artificial intelligence to improve binding and stability of the antibody among many other applications of this amazing tool. Listen below and click here to browse our archives!
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