Rens Dimmendaal & Banjong Raksaphakdee / Better Images of AI / Medicines (flipped) / Licenced by CC-BY 4.0 The potential of using artificial intelligence in drug discovery and development has sparked both excitement and skepticism among scientists, investors and the general public. "Artificial intelligence is taking over drug development," claim some companies and researchers. Over the past few years, interest in using AI to design drugs and optimize clinical trials has driven a surge in research and investment. AI-driven platforms like AlphaFold, which won the 2024 Nobel Prize for its ability to predict the structure of proteins and design new ones, showcase AI's potential to accelerate drug development. AI in drug discovery is "nonsense," warn some industry veterans. They urge that "AI's potential to accelerate drug discovery needs a reality check," as AI-generated drugs have yet to demonstrate an ability to address the 90% failure rate of new drugs in clinical trials.

A novel artificial intelligence model could significantly improve the accuracy and reduce the time and cost of the drug development process. Between identifying a potential therapeutic compound and U. S. Food and Drug Administration (FDA) approval of a new drug is an arduous journey that can take well over a decade and cost upwards of a billion dollars. A team of researchers at the CUNY Graduate Center has developed a novel artificial intelligence model that could significantly improve the accuracy and reduce the time and cost of the drug development process. As described in a paper to be published today (October 17) in Nature Machine Intelligence, the new model, called CODE-AE, can screen novel drug compounds to accurately predict efficacy in humans. In tests, it was also able to theoretically identify personalized drugs for over 9,000 patients that could better treat their conditions.

Artificial intelligence (AI)-enabled data collection and management can be a game changer for life sciences companies in the drug development process. Once the stuff of science fiction, AI has made the leap to practical reality. Yet, to date, most life sciences companies have only scratched the surface of AI's potential. One area that holds particular promise: digital data flow automation for clinical trials. With the power of AI, companies can rapidly digitize clinical-trial processes so they can complete studies faster.

Will artificial intelligence (AI) change the traditional trial–and–error drug development process and become a revolutionary force in the pharmaceutical sector? Active learning and interpretable AI are the two critical paradigms that lead to the positive answer, according to a perspective article recently published in Health Data Science, a Science Partner Journal. "Promising progress has been made in using AI for drug design recently. However, we are still far from certain that these early results could be translated to more effective drugs with a high success rate," said co-author Jianzhu Ma, Ph.D., a specialist and associate professor of artificial intelligence at Peking University. "How to harness the value of data is the key to building successful AI for drug development." The authors pointed out that the major limitation of conventional AI-aided drug development is its linear paradigm. Without continuous feedback from the downstream experimental results, the preceding step of AI model prediction is only "educated guesses".

Although the Covid-19 pandemic persisted throughout the year, the healthcare industry continued to fulfill its duty based on experiences of last year. While going all out to develop Covid-19 treatments and vaccines, the industry tried to graft new technologies, including AI, to promote the sector's development. In the process, the industry revealed problems requiring correction, such as manipulating raw materials and impurities caught in antihypertensive drugs. Still, the sector continued to improve itself in keeping with the changing global healthcare industry amid the Covid-19 crisis. Korea Biomedical Review has compiled the five biggest industry stories in 2021.

IBM research contributed two platforms to this project. The RXN for chemistry uses natural language processing to automate synthetic chemistry and AI to make predictions about the success rate of the compounds used in the medicines. The company also uses an automated platform RoboRXN for molecule synthesis. The other company Arctoris used its automated platform Ulysses for the project which uses robots and digital data to conduct lab experiments in cell biology, molecular biology, and biophysics. And the experiments conducted by Ulysses generated 100 times more data in comparison to the industry-standard manual methods.

The continued contribution of the drug development community toward improving the quality of lives of patients, researchers, and the public at large, is and will continue to be highly dependent upon the careful execution of strategies to make vast amounts of data meaningful and usable. This is achievable by pairing data with powerful analytics and then using those insights to develop safe and effective processes and products. Although the drug development enterprise is undergoing major transformation, literature about what the sector should do to support and prepare its workforce for these changes is scant. What follows is a discussion of original research conducted by the Tufts Center for the Study of Drug Development (Tufts CSDD) to address workforce development in the era of digitization. The research is primarily based on an in-depth discussion with thought leaders and senior executives. Tufts CSDD identified recurring themes for discussion in articles in academic journals and the trade press between 2015 and 2019. Discussion topics included: (1) challenges and opportunities caused by the sector's digital transformation, (2) skills and competencies of future drug development professionals, (3) new roles that are expected to emerge within drug development, (4) changes in talent recruitment and retention practices, and (5) the reshaping of corporate mindsets and cultures to become digitally proficient organizations.

Genuity Science, a genomics and data-sourcing, analytics and insights organization, announced today that Tom Chittenden, PhD, DPhil, PStat, Chief Data Science Officer and Founding Director of the Genuity Science Advanced Artificial Intelligence Research Laboratory, is a winner of the Artificial Intelligence Excellence Awards presented by the Business Intelligence Group. The awards identify organizations, products, and people who bring AI to life and apply it to solve real problems. Chittenden and team were recognized for the use of novel, biologically-validated and published AI capabilities to address key areas of risk and opportunity at virtually any point in the drug development process. Despite the COVID-19 pandemic, Chittenden and Genuity Science continued their goal of using genetics to help understand diseases in new ways so that they can be treated in new ways. This past year, in collaboration with Professor Michael Simons and his team at Yale University Medical School, the Genuity Science Advanced AI Research Laboratory used a new generative AI approach to discover, in mice studies, a novel pathobiological mechanism that causally underpins aortic aneurysm.

Sinai School of Medicine, Stanford University and the Northern California Institute for Research and Education, IBM Research is undertaking a new research initiative funded by the National Institutes of Health. As part of a broader $99 million, 5-year research initiative spanning multiple public and private organizations and research institutions, this work will tap into AI and big data to help better identify individuals at high-risk of developing schizophrenia, a serious mental illness affecting how a person thinks, feels and behaves. Schizophrenia is often characterized by alterations to a person's thoughts, feelings and behaviors, which can include a loss of contact with reality known as psychosis. A better understanding of how this disease could be detected prior to psychosis could help to postpone or even prevent the transition to psychosis, as well as possibly improve outcomes. The project is a component of the Accelerating Medicines Partnership (AMP), a collaboration between the National Institutes of Health (NIH), the U.S. Food and Drug Administration (FDA), pharmaceutical companies, biotech firms and nonprofit organizations.

This ebook, based on the latest ZDNet / TechRepublic special feature, looks at how the industry cloud has taken off and big businesses have been built by the likes of Veeva, Rootstock and others. Hospitals and health systems are using cloud services to power a digital transformation, but pharmaceutical companies are using the cloud to revamp their business operations in even more fundamental ways. Computational drug discovery uses a combination of cloud computing and artificial intelligence to make the drug development process faster and cheaper. The big drug companies are taking advantage of improvements in AI and the computational power of the cloud to test this new approach. Pharma companies are using this tech-driven process to develop traditional medications as well as completely new categories that work at the level of DNA and RNA to stop disease. Hyperscale cloud providers are vital new partners in this form of drug development.