Artificial intelligence (AI) has sparked immense interest in drug discovery, but most current approaches only digitize existing high-throughput experiments. They remain constrained by conventional pipelines. As a result, they do not address the fundamental challenges of predicting drug effects in humans. Similarly, biomedical digital twins, largely grounded in real-world data and mechanistic models, are tailored for late-phase drug development and lack the resolution to model molecular interactions or their systemic consequences, limiting their impact in early-stage discovery. This disconnect between early discovery and late development is one of the main drivers of high failure rates in drug discovery. The true promise of AI lies not in augmenting current experiments but in enabling virtual experiments that are impossible in the real world: testing novel compounds directly in silico in the human body. Recent advances in AI, high-throughput perturbation assays, and single-cell and spatial omics across species now make it possible to construct programmable virtual humans: dynamic, multiscale models that simulate drug actions from molecular to phenotypic levels. By bridging the translational gap, programmable virtual humans offer a transformative path to optimize therapeutic efficacy and safety earlier than ever before. This perspective introduces the concept of programmable virtual humans, explores their roles in a new paradigm of drug discovery centered on human physiology, and outlines key opportunities, challenges, and roadmaps for their realization.

Accelerating drug discovery with Artificial: a whole-lab orchestration and scheduling system for self-driving labs Y ao Fehlis, Paul Mandel, Charles Crain, Betty Liu, David Fuller a a Artificial Inc.,Abstract Self-driving labs are transforming drug discovery by enabling automated, AI-guided experimentation, but they face challenges in orchestrating complex workflows, integrating diverse instruments and AI models, and managing data e fficiently. Artificial addresses these issues with a comprehensive orchestration and scheduling system that unifies lab operations, automates workflows, and integrates AI-driven decision-making. By incorporating AI / ML models like NVIDIA BioNeMo--which facilitates molecular interaction prediction and biomolecular analysis--Artificial enhances drug discovery and accelerates data-driven research. Through real-time coordination of instruments, robots, and personnel, the platform streamlines experiments, enhances reproducibility, and advances drug discovery. Introduction The landscape of drug discovery has long been characterized by a multitude of challenges, including the high costs of research and development, lengthy timelines, and a significant rate of failure during clinical trials (Blanco-Gonzalez et al., 2023; Udegbe et al., 2024; Khanna, 2012; Mo ffat et al., 2017).

Inventor and futurist Ray Kurzweil, researcher and Brookings Institution fellow Chinasa T. Okolo, director of the U.S. Artificial Safety Institute (AISI) Elizabeth Kelly, and Cognizant CEO Ravi Kumar S, discussed the transformative power of AI during a panel at a TIME100 Impact Dinner in San Francisco on Monday. During the discussion, which was moderated by TIME's editor-in-chief Sam Jacobs, Kurzweil predicted that we will achieve Artificial General Intelligence (AGI), a type of AI that might be smarter than humans, by 2029. "Nobody really took it seriously until now," Kurzweil said about AI. "People are convinced it's going to either endow us with things we'd never had before, or it's going to kill us." Cognizant sponsored Monday's event, which celebrated the 100 most influential people leading change in AI. Jacobs probed the four panelists--three of whom were named to the 2024 list--about the opportunities and challenges presented by AI's rapid advancement.

Drug discovery and development is a highly complex and costly endeavor, typically requiring over a decade and substantial financial investment to bring a new drug to market. Traditional computer-aided drug design (CADD) has made significant progress in accelerating this process, but the development of quantum computing offers potential due to its unique capabilities. This paper discusses the integration of quantum computing into drug discovery and development, focusing on how quantum technologies might accelerate and enhance various stages of the drug development cycle. Specifically, we explore the application of quantum computing in addressing challenges related to drug discovery, such as molecular simulation and the prediction of drug-target interactions, as well as the optimization of clinical trial outcomes. By leveraging the inherent capabilities of quantum computing, we might be able to reduce the time and cost associated with bringing new drugs to market, ultimately benefiting public health.

Multinational pharmaceutical and biotech company Pfizer and AI-powered data company Tempus announced a multiyear strategic alliance to utilize AI and machine learning to inform drug discovery and development in oncology. Pfizer will leverage Tempus' library of de-identified data to accelerate therapeutic development in oncology. It will also use Tempus' AI-driven companion diagnostic offerings and clinical trial-matching program to support therapeutic research and development. "Pfizer shares our commitment to bringing novel treatments to patients faster, and we look forward to working together to usher in the next generation of oncology therapeutics," Eric Lefkofsky, founder and CEO of Tempus, said in a statement. "This is the third strategic collaboration Tempus has established with a global pharmaceutical leader in the last year, as we believe that combining our technological capabilities with pharma's deep R&D expertise will get us much closer in realizing the full potential of precision medicine."

To discover new drugs is to seek and to prove causality. As an emerging approach leveraging human knowledge and creativity, data, and machine intelligence, causal inference holds the promise of reducing cognitive bias and improving decision making in drug discovery. While it has been applied across the value chain, the concepts and practice of causal inference remain obscure to many practitioners. This article offers a non-technical introduction to causal inference, reviews its recent applications, and discusses opportunities and challenges of adopting the causal language in drug discovery and development.

Alphabet subsidiary and precision health company Verily recently announced a breakthrough in its AI drug discovery GPCR research collaboration with Sosei Heptares. A mere six months ago Verily launched the study with Sosei Heptares – a global leader in GPCR structure-based drug design – with an aim to "prioritise protein targets for therapeutic targeting in immune-mediated disease". Now, Verily has announced that early results from its "next generation immune mapping technology" Immune Profiler platform have already identified "more effective therapeutic options against G protein-coupled receptors (GPCR) in autoimmune and other immune-mediated diseases". The companies hope that in the year to come those data targets will be entered for validation, hit generation, and lead selection. With approximately one third of all current FDA-approved drugs targeting GPCRs, Verily/Sosei Heptares are looking to expedite GPCR research within not only immunology, but also gastroenterology and immuno-oncology as well, and the latest data bodes well for future development of therapeutic options in these areas.

AI pathology specialist PathAI has announced a multi-year expanded collaboration agreement with Bristol Myers Squibb. The initial work within this extended agreement will focus on key translational research in oncology, fibrosis, and immunology, with an overall goal to forward these into clinical trials. PathAI develops quantitative pathology algorithms for drug discovery and development. "We look forward to collaborating with PathAI to expand the potential application of AI in the drug development process to include translational research, clinical trials, and diagnostic advancements," said Robert Plenge, MD, PhD, senior vice president of Bristol Myers Squibb, head of Immunology, Cardiovascular, and Fibrosis Thematic Research Center, and head of Translational Medicine. "We feel that PathAI will be a productive collaborator given digital pathology represents a growing area for BMS, PathAI is a leader in the field, and the fact that we have a long-standing productive relationship with the company," he added.

In this post, we summarise the final two invited talks from the International Conference on Machine Learning (ICML 2022). These presentations covered estimation and inference for causal effects, and machine learning for drug discovery and development. Guido's talk covered the topic of estimation and inference for causal effects in panel data settings, in particular focussing on synthetic control methods and difference-in-difference methods. These methods are very popular in the empirical literature in economics, but many questions remain concerning causal effects in these settings. There has been a lot of recent theoretical work trying to improve practices in this field.

Rapid new drug discovery had never been more critical in a world with an aging population and increased instances of infectious diseases. While traditional lab methods have proven reliable, the recent covid-19 pandemic has shown the need for further innovation. The global drug discovery market size was valued at US$ 74.96 billion in 2021.[1] Despite such massive investments, the number of new drugs approved by the FDA remains low. Current drug discovery methods are slow, expensive, dominated by big pharma, and require cruel animal testing procedures.