Drug discovery companies such as Insilico Medicine are using deepfake AI technology to design new molecules that can help treat diseases. Intel made a splash earlier this week when it unveiled its latest technology that can detect a deepfake in real-time with 96pc accuracy. AI such as this can help organisations around the world to prevent the spread of misinformation and protect themselves from cybercrime. But not all kinds of deepfakes are bad. The advancement of any emerging technology brings with it positive and negative uses – and the future of healthcare certainly has much to gain from deepfakes.

Safe and effective medications are needed to meet the medical needs of billions worldwide, which are driven by aging populations and increasing insight into disease burden. However, getting a novel drug to the market currently takes 13–15 years and US$2–3 billion, on average1. Faced with skyrocketing costs and high failure rates, researchers are looking at ways to make drug discovery and development more efficient through automation, artificial intelligence (AI) and new data modalities2,3. AI has become woven into therapeutic discovery since the emergence of deep learning4. It stands out as an approach to guide discovery5 by finding and extracting actionable predictions that lend themselves to hypotheses testable in the laboratory.

Data science and artificial intelligence are adding a new dimension to drug discovery and development, emphasizing computation and machine learning. Given this shift, pharmaceutical companies are actively building infrastructure, data, tools, and teams to bring together data scientists with biology and life science experts. Pharma and biotech innovation offer a glimpse into how large organizations integrate AI tools and techniques with traditional subject matter experts who possess a deep understanding of the underlying problems to be solved. To gain an insider's perspective on how pharma companies use AI and machine learning, I invited Dr. Bülent Kızıltan to join episode #717 of the CXOTalk series of conversations with people shaping our world. He is Head of Causal & Predictive Analytics, Data Science & AI, at the Novartis AI Innovation Center.

Many of today's most urgent problems demand new molecules and materials, from antimicrobial drugs to fight superbugs and antivirals to treat novel pandemics to more sustainable photosensitive coatings for semiconductors and next-generation polymers to capture carbon dioxide right at its source. We can design these from scratch, using AI to expedite the otherwise expensive and slow process, or we can tweak existing molecules to fine-tune the properties we care about -- such as toxicity, activity, or stability. Starting from a known molecule is like getting a head start on the design and production of candidate molecules, as we know they have some of the characteristics we need, and we can use existing knowledge and manufacturing pipelines to synthesize and test them down the line. The challenge in this process, called molecular optimization, is that tweaking an existing molecule can produce a huge number of variants. They won't all have the desired properties, and evaluating them empirically to find those that do would take too much time and money to be feasible.

By 2023, the pharmaceutical industry is projected to increase up to $1.5 trillion dollars. Throughout the past years, there has been huge growth within the Pharma industry that will continue into 2022. Artificial Intelligence (AI) is becoming a universal term in many industries today. AI uses learned machine intelligence to perform tasks and make predictions. AI within the pharma industry has many purposes that can accelerate the discovery and development of new drugs.

AbCellera and Gilead Sciences announced a new multitarget antibody discovery collaboration building on their previous infectious disease partnership from 2019. AbCellera is now starting to reap the fruits of its labor, as its successful antibody discovery programs earned the company $203 million in revenue in the first quarter of this year, including $178 million in milestones and royalties. Gilead Sciences also announced a very interesting partnership during the first half of 2021, this time with Gritstone Oncology to create a vaccine-based immunotherapy as a cure for HIV. Under the terms of the deal, Gilead is paying Gritstone $30 million upfront and a $30 million equity investment, and potentially an additional $725 million in regulatory and commercial milestones and royalties on net sales. BenevolentAI and AstraZeneca have been collaborating closely since 2019 to use AI and machine learning for the discovery and development of new treatments for chronic kidney disease (CKD) and idiopathic pulmonary fibrosis (IPF).

Artificial intelligence (AI) is reshaping healthcare, and its use is becoming a reality in many medical fields and specialties. AI, machine learning (ML), natural language processing (NLP) and deep learning (DL) enable healthcare stakeholders and medical professionals to identify healthcare needs and solutions faster with more accuracy, using data patterns to make informed medical or business decisions quickly. AI is able to analyze large amounts of data stored by healthcare organizations in the form of images, clinical research trials and medical claims, and can identify patterns and insights often undetectable by manual human skill sets. AI algorithms are "taught" to identify and label data patterns, while NLP allows these algorithms to isolate relevant data. With DL, the data is analyzed and interpreted with the help of extended knowledge by computers.

Deep Genomics, the Toronto-based AI therapeutics startup, has made the first-ever discovery of a diseases treatment and drug candidate using artificial intelligence. The startup announced on Wednesday that its propriety AI-based drug discovery platform has identified a novel treatment target and corresponding drug candidate for Wilson disease, a rare and potentially life-threatening genetic disorder. "Our AI systems can figure out how diseases are caused and how to fix those diseases much more rapidly than humans ever could." "This is the amazing accomplishment for the team," Brendan Frey, founder and CEO of Deep Genomics, told BetaKit. He noted that part of Deep Genomic's goal is to "help everyone in the world" use the discovery and technology its developing in Canada to support discovery and development more broadly.

Swiss pharma giant Novartis has partnered with tech giant Microsoft and launched its Novartis AI innovation lab as a means to leverage data and artificial intelligence to transform drug discovery and development. The new Novartis AI lab aims to significantly bolster Novartis AI capabilities from research through commercialization and help accelerate the discovery and development of transformative medicines for patients worldwide, the company announced Monday. As part of the strategic collaboration between the two companies, Novartis and Microsoft have committed to a multi-year research and development effort with a focus on AI empowerment and AI exploration. With AI empowerment, Novartis said the goal is for every employee in the lab to have the AI program on their computer. Using Novartis' datasets and Microsoft's AI solutions, the company said the lab will create new AI models and applications that can augment their discovery and development capabilities.

Drug discovery and development has a long history and dates back to the early days of human civilization. In those ancient times, drugs were not just used for physical remedies but were also associated with religious and spiritual healing. Many early medicinal treatments originated in China, as early as 3500 BC, and many of these early recipes are still available today. Indian folk remedies also existed 3000 to 5000 years ago, and are referred to as Ayurvedic medicine. Some Ayurvedic ingredients are still used in Western medicine today.