Jun Wang博士是罗格斯大学厄内斯特·马里奥药学院的药剂学教授。他的研究致力于抗病毒治疗药物的发现与开发，特别关注病毒半胱氨酸蛋白酶、去泛素化酶以及其他关键的病毒酶和蛋白。他的实验室整合了基于结构的药物设计、共价和非共价抑制剂发现、生化与细胞检测开发、抗病毒验证、药代动力学评估以及人工智能驱动的虚拟筛选，以推进新的抗病毒候选药物。王博士的团队在发现靶向冠状病毒木瓜样蛋白酶（包括SARS-CoV-2 PLpro）的一类先导抑制剂方面做出了重要贡献，并开发了具有明确靶点结合、细胞活性及体内药效的机制验证抗病毒系列。他更广泛的研究计划还包括针对肠道病毒、冠状病毒及其他新兴或再发病毒病原体的抗病毒发现。除实验药物化学外，王博士的实验室积极开发并应用计算及AI辅助的药物发现方法，包括超大规模虚拟筛选、主动学习、分子对接、蛋白质-配体建模及结构引导的先导化合物优化。这些方法与直接生物合成及严谨的实验验证相结合，加速了新型化学实体的鉴定。王博士因其在生物有机和药物化学领域的贡献获得了国内外的认可，包括2026年Tetrahedron生物有机与药物化学青年研究员奖以及2026年美国化学会药物化学分部Robert M. Scarborough药物化学优秀奖。他的工作旨在建立广泛适用的抗病毒发现平台，能够快速响应当前及未来的病毒威胁。
Dr. Jun Wang is a Professor of Medicinal Chemistry at the Ernest Mario School of Pharmacy, Rutgers University. His research focuses on the discovery and development of antiviral therapeutics, with particular emphasis on viral cysteine proteases, deubiquitinases, and other essential viral enzymes and proteins. His laboratory integrates structure-based drug design, covalent and noncovalent inhibitor discovery, biochemical and cellular assay development, antiviral validation, pharmacokinetic evaluation, and AI-enabled virtual screening to advance new antiviral drug candidates.
Dr. Wang’s group has made important contributions to the discovery of first-in-class inhibitors targeting coronavirus papain-like proteases, including SARS-CoV-2 PLpro, and has developed mechanistically validated antiviral series with defined target engagement, cellular activity, and in vivo efficacy. His broader research program also includes antiviral discovery against enteroviruses, coronaviruses, and other emerging or re-emerging viral pathogens.
In addition to experimental medicinal chemistry, Dr. Wang’s laboratory actively develops and applies computational and AI-assisted approaches for drug discovery, including ultralarge-scale virtual screening, active learning, molecular docking, protein–ligand modeling, and structure-guided lead optimization. These approaches are integrated with direct-to-biology synthesis and rigorous experimental validation to accelerate the identification of novel chemical matter.
Dr. Wang has received national and international recognition for his contributions to bioorganic and medicinal chemistry, including the 2026 Tetrahedron Young Investigator Award for Bioorganic and Medicinal Chemistry and the 2026 ACS Division of Medicinal Chemistry Robert M. Scarborough Award for Excellence in Medicinal Chemistry. His work aims to build broadly applicable antiviral discovery platforms that can respond rapidly to current and future viral threats.
## 讲座简介
Emerging and re-emerging viral infections continue to pose major threats to global health, yet the development of direct-acting antivirals remains slow, costly, and technically challenging. Artificial intelligence is beginning to reshape antiviral drug discovery by enabling rapid analysis of large chemical spaces, prioritization of novel chemical matter, and integration of structural, biochemical, cellular, and pharmacological data. In this presentation, I will discuss how AI-enabled approaches can be combined with medicinal chemistry, structure-based drug design, and rigorous experimental validation to accelerate the discovery of antiviral therapeutics. Our laboratory focuses on viral cysteine proteases, deubiquitinases, and other essential viral targets, including coronavirus papain-like proteases and enterovirus proteins. We integrate ultralarge-scale virtual screening, active learning, molecular docking, protein–ligand modeling, and direct-to-biology synthesis with biochemical assays, cellular target engagement studies, antiviral assays, mechanism-of-action validation, and pharmacokinetic evaluation. This workflow allows rapid progression from computational hit identification to experimentally validated inhibitors with defined potency, selectivity, cellular activity, and translational potential. I will highlight examples from our discovery of first-in-class inhibitors targeting SARS-CoV-2 papain-like protease and related antiviral programs. These studies illustrate both the opportunities and limitations of AI in drug discovery: AI can improve speed, scale, and prioritization, but high-impact antiviral discovery still depends on target biology, assay quality, medicinal chemistry judgment, and orthogonal validation. Together, these integrated platforms provide a practical framework for developing new antivirals and preparing for future viral outbreaks.
Emerging and re-emerging viral infections continue to pose major threats to global health, yet the development of direct-acting antivirals remains slow, costly, and technically challenging. Artificial intelligence is beginning to reshape antiviral drug discovery by enabling rapid analysis of large chemical spaces, prioritization of novel chemical matter, and integration of structural, biochemical, cellular, and pharmacological data. In this presentation, I will discuss how AI-enabled approaches can be combined with medicinal chemistry, structure-based drug design, and rigorous experimental validation to accelerate the discovery of antiviral therapeutics.
Our laboratory focuses on viral cysteine proteases, deubiquitinases, and other essential viral targets, including coronavirus papain-like proteases and enterovirus proteins. We integrate ultralarge-scale virtual screening, active learning, molecular docking, protein–ligand modeling, and direct-to-biology synthesis with biochemical assays, cellular target engagement studies, antiviral assays, mechanism-of-action validation, and pharmacokinetic evaluation. This workflow allows rapid progression from computational hit identification to experimentally validated inhibitors with defined potency, selectivity, cellular activity, and translational potential.
I will highlight examples from our discovery of first-in-class inhibitors targeting SARS-CoV-2 papain-like protease and related antiviral programs. These studies illustrate both the opportunities and limitations of AI in drug discovery: AI can improve speed, scale, and prioritization, but high-impact antiviral discovery still depends on target biology, assay quality, medicinal chemistry judgment, and orthogonal validation. Together, these integrated platforms provide a practical framework for developing new antivirals and preparing for future viral outbreaks.
