Programmable Virtual Humans Toward Human Physiologically-Based Drug Discovery

• URL: http://arxiv.org/abs/2507.19568v1
• Date: Fri, 25 Jul 2025 16:40:57 GMT
• Title: Programmable Virtual Humans Toward Human Physiologically-Based Drug Discovery
• Authors: You Wu, Philip E. Bourne, Lei Xie,
• Abstract summary: Current approaches only digitize existing high- throughput experiments.<n>They remain constrained by conventional pipelines.<n> programmable virtual humans offer a transformative path to optimize therapeutic efficacy and safety earlier than ever before.
• Score: 9.308864711259357
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Position: Intelligent Science Laboratory Requires the Integration of Cognitive and Embodied AI [98.19195693735487]
  We propose the paradigm of Intelligent Science Laboratories (ISLs)<n>ISLs are a multi-layered, closed-loop framework that deeply integrates cognitive and embodied intelligence.<n>We argue that such systems are essential for overcoming the current limitations of scientific discovery.
  arXiv  Detail & Related papers  (2025-06-24T13:31:44Z)
• Physical formula enhanced multi-task learning for pharmacokinetics prediction [54.13787789006417]
  A major challenge for AI-driven drug discovery is the scarcity of high-quality data. We develop a formula enhanced mul-ti-task learning (PEMAL) method that predicts four key parameters of pharmacokinetics simultaneously. Our experiments reveal that PEMAL significantly lowers the data demand, compared to typical Graph Neural Networks.
  arXiv  Detail & Related papers  (2024-04-16T07:42:55Z)
• DiffuseBot: Breeding Soft Robots With Physics-Augmented Generative Diffusion Models [102.13968267347553]
  We present DiffuseBot, a physics-augmented diffusion model that generates soft robot morphologies capable of excelling in a wide spectrum of tasks. We showcase a range of simulated and fabricated robots along with their capabilities.
  arXiv  Detail & Related papers  (2023-11-28T18:58:48Z)
• Neural Operators for Accelerating Scientific Simulations and Design [85.89660065887956]
  An AI framework, known as Neural Operators, presents a principled framework for learning mappings between functions defined on continuous domains. Neural Operators can augment or even replace existing simulators in many applications, such as computational fluid dynamics, weather forecasting, and material modeling.
  arXiv  Detail & Related papers  (2023-09-27T00:12:07Z)
• Artificial Intelligence for Drug Discovery: Are We There Yet? [0.08306867559432653]
  Drug discovery is adapting to novel technologies such as data science, informatics, and artificial intelligence (AI) to accelerate effective treatment development. This review discusses the use of AI in the three pillars of drug discovery: diseases, targets, and therapeutic modalities, with a focus on small molecule drugs.
  arXiv  Detail & Related papers  (2023-07-13T01:51:26Z)
• The Future of Fundamental Science Led by Generative Closed-Loop Artificial Intelligence [67.70415658080121]
  Recent advances in machine learning and AI are disrupting technological innovation, product development, and society as a whole. AI has contributed less to fundamental science in part because large data sets of high-quality data for scientific practice and model discovery are more difficult to access. Here we explore and investigate aspects of an AI-driven, automated, closed-loop approach to scientific discovery.
  arXiv  Detail & Related papers  (2023-07-09T21:16:56Z)
• Accelerating science with human-aware artificial intelligence [2.7786142348700658]
  We show that incorporating the distribution of human expertise by training unsupervised models dramatically improves (up to 400%) AI prediction of future discoveries. These models succeed by predicting human predictions and the scientists who will make them. Accelerating human discovery or probing its blind spots, human-aware AI enables us to move toward and beyond the contemporary scientific frontier.
  arXiv  Detail & Related papers  (2023-06-02T12:43:23Z)
• Recent Developments in Structure-Based Virtual Screening Approaches [0.0]
  We give an introduction to the foundations of structure-based virtual screenings. We outline key principles, recent success stories, new methods, available software, and promising future research directions. Virtual screenings have an enormous potential for the development of new small-molecule drugs.
  arXiv  Detail & Related papers  (2022-11-06T19:28:25Z)
• Artificial Intelligence based Autonomous Molecular Design for Medical Therapeutic: A Perspective [9.371378627575883]
  Domain-aware machine learning (ML) models have been increasingly adopted for accelerating small molecule therapeutic design. We present the most recent breakthrough achieved by each of the components, and how such autonomous AI and ML workflow can be realized.
  arXiv  Detail & Related papers  (2021-02-10T00:43:46Z)
• RoboTHOR: An Open Simulation-to-Real Embodied AI Platform [56.50243383294621]
  We introduce RoboTHOR to democratize research in interactive and embodied visual AI. We show there exists a significant gap between the performance of models trained in simulation when they are tested in both simulations and their carefully constructed physical analogs.
  arXiv  Detail & Related papers  (2020-04-14T20:52:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.