LANCASTER, Calif.--(BUSINESS WIRE)--Simulations Plus, Inc. (Nasdaq: SLP) (“Simulations Plus”), a leading provider of biosimulation, simulation-enabled performance and intelligence solutions, and medical communications to the biopharma industry, today announced the award of a new research grant from the National Institutes of Health (NIH), secured in partnership with the University of Southern California (USC) Alfred E.

加利福尼亚州兰开斯特市——（商业新闻短讯）——Simulations Plus，Inc.（纳斯达克：SLP）（“Simulations Plus”）是生物模拟、模拟性能和智能解决方案以及生物制药行业医疗通信的领先提供商，今天宣布授予美国国立卫生研究院（National Institutes of Health，NIH）的新研究资助，该资助由美国南加州大学（University of Southern California，USC）阿尔弗雷德·E。

Mann School of Pharmacy and Pharmaceutical Sciences. The grant will be used to evaluate novel computational methods that account for water-ligand interactions in drug discovery and that integrate with the Artificial Intelligence-driven Drug Design (AIDD) module in ADMET Predictor® to offer a first-of-its-kind ligand-based virtual screening (LBVS) solution for pharmaceutical companies..

曼恩药学与药物科学学院。。。

For this award, Dr. Ian Haworth, Associate Professor and Vice Chair of Pharmacology and Pharmaceutical Sciences at the USC Mann School, and his lab will apply their previously developed algorithm (WATGEN) for the prediction of water positions in the unbound protein and protein-ligand complex. With support from the data scientists and software engineers at Simulations Plus, they will apply machine learning (ML) approaches to predict the pharmacophore features that will be used in ADMET Predictor’s proprietary 3D shape and feature matching algorithm..

。在Simulations Plus的数据科学家和软件工程师的支持下，他们将应用机器学习（ML）方法来预测药效团特征，这些特征将用于ADMET Predictor专有的3D形状和特征匹配算法。。

“Identifying chemicals with shapes and characteristics similar to those that bind drug targets has been invaluable in drug discovery and development. However, the retention or displacement of water molecules during formation of the protein-ligand interface plays a significant role in determining ligand binding.

“识别形状和特征与结合药物靶标相似的化学物质在药物发现和开发中具有无价的价值。然而，在蛋白质-配体界面形成过程中水分子的保留或置换在确定配体结合中起着重要作用。

This has often been overlooked in existing software programs, including LBVS algorithms,” said Dr. Noam Morningstar-Kywi, Scientist II at Simulations Plus and a key investigator for this grant. “Our goal is to develop new approaches that combine ML and validated 3D-based calculations to incorporate these essential water molecules into LBVS, enhancing current methods and enabling researchers to accelerate the discovery of better and more effective drugs.”.

这在包括LBVS算法在内的现有软件程序中经常被忽视，”Simulations Plus的科学家II Noam Morningstar Kywi博士说，他是这项资助的关键研究人员。“我们的目标是开发新的方法，将ML和经过验证的基于3D的计算相结合，将这些必需的水分子纳入LBV，增强当前的方法，并使研究人员能够加速发现更好，更有效的药物。”。

Dr. Haworth added, “We will harness the power of structure-based approaches, including the detailed information of protein-ligand and protein-water interactions, and combine them with the speed and accuracy associated with ligand-based similarity scoring methods. This project is a powerful collaboration between industry and academia that drives research from the lab into real-world applications, promising exciting, tangible results that could transform the field.”.

霍沃斯博士补充道：“我们将利用基于结构的方法的力量，包括蛋白质-配体和蛋白质-水相互作用的详细信息，并将它们与基于配体的相似性评分方法相关的速度和准确性相结合。该项目是工业界和学术界之间的强大合作，将实验室的研究转化为现实世界的应用，有望取得令人兴奋的有形成果，从而改变该领域。”。

The team at Simulations Plus will productize the updated methods into the ADMET Predictor platform and validate it by designing drugs against defined targets using the AIDD module. Selected compounds will be synthesized and tested experimentally to highlight the technology’s applications.

Simulations Plus的团队将更新的方法生产到ADMET Predictor平台中，并通过使用AIDD模块针对定义的目标设计药物来验证它。选定的化合物将进行合成和实验测试，以突出该技术的应用。

“As a drug discovery scientist, I am particularly excited to apply the NIH funding towards this innovative technology to design and test new compounds against several clinically relevant targets. We have the potential to dramatically reduce the Design-Make-Test-Analyze (DMTA) cycle of drug discovery,” said Dr.

“作为一名药物发现科学家，我特别兴奋地将NIH的资金用于这项创新技术，以针对几个临床相关目标设计和测试新化合物。我们有可能大大缩短药物发现的设计-制造-测试-分析（DMTA）周期，”Dr。

Jeremy Jones, Principal Scientist at Simulations Plus and principal investigator for this grant. “We are committed to driving impactful advancements that benefit our stakeholders and the global communities we serve, and we eagerly anticipate future collaborations that continue to create value and foster growth.”.

Jeremy Jones是Simulations Plus的首席科学家，也是这项资助的首席研究员。“我们致力于推动有影响力的进步，使我们的利益相关者和我们服务的全球社区受益，我们热切期待未来的合作，继续创造价值和促进增长。”。

The information presented in this press release is supported by the National Institute of General Medical Sciences of the National Institutes of Health under Award Number R43GM156103. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health..

本新闻稿中提供的信息得到了美国国立卫生研究院国家普通医学科学研究所的支持，奖项编号为R43GM156103。内容完全由作者负责，不一定代表美国国立卫生研究院的官方观点。。

About the Haworth laboratory in the USC Mann School at the University of Southern California

关于南加州大学南加州大学曼学院的霍沃斯实验室

Dr. Ian S. Haworth, a professor at USC, holds a PhD in physical organic chemistry from the University of Liverpool and conducted postdoctoral research at Oxford University. Since joining USC in 1992, his research has focused on the dynamics of molecular interactions of ligands with nucleic acids and proteins, combining chemistry, biology, and computational sciences.

南加州大学教授伊恩·霍沃斯博士拥有利物浦大学物理有机化学博士学位，并在牛津大学进行博士后研究。自1992年加入南加州大学以来，他的研究集中在配体与核酸和蛋白质的分子相互作用动力学上，结合了化学，生物学和计算科学。

Dr. Haworth’s laboratory has developed innovative algorithms for nucleic acid structure building, molecular solvation, transporter protein analysis, and MHC-peptide-TCR association. His work, supported by federal and non-federal funding, has led to the publication of many papers in these areas..

霍沃斯博士的实验室开发了用于核酸结构构建，分子溶剂化，转运蛋白分析和MHC肽TCR关联的创新算法。他的工作得到了联邦和非联邦资金的支持，在这些领域发表了许多论文。。

About Simulations Plus, Inc.

关于Simulations Plus，Inc。

With more than 25 years of experience serving clients globally, Simulations Plus stands as a premier provider in the biopharma sector, offering advanced software and consulting services that enhance drug discovery, development, research, clinical trial operations, regulatory submissions, and commercialization.

Simulations Plus拥有超过25年的全球客户服务经验，是生物制药领域的领先供应商，提供先进的软件和咨询服务，可增强药物发现、开发、研究、临床试验操作、监管提交和商业化。

Our comprehensive biosimulation solutions integrate artificial intelligence/machine learning (AI/ML), physiologically based pharmacokinetics, physiologically based biopharmaceutics, quantitative systems pharmacology/toxicology, and population PK/PD modeling approaches. We also deliver simulation-enabled performance and intelligence solutions alongside medical communications support for clinical and commercial drug development.

我们全面的生物模拟解决方案集成了人工智能/机器学习（AI/ML），基于生理的药代动力学，基于生理的生物制药，定量系统药理学/毒理学和群体PK/PD建模方法。我们还提供支持模拟的性能和智能解决方案，以及用于临床和商业药物开发的医疗通信支持。

Our cutting-edge technology is licensed and utilized by leading pharmaceutical, biotechnology, and regulatory agencies worldwide. For more information, visit our website at www.simulations-plus.com. Follow us on LinkedIn | X | YouTube.

我们的尖端技术已获得全球领先的制药、生物技术和监管机构的许可和使用。有关更多信息，请访问我们的网站www.simulations-plus.com。在LinkedIn | X | YouTube上关注我们。

Environmental, Social, and Governance (ESG)

环境、社会和治理（ESG）

We focus our Environmental, Social, and Governance (ESG) efforts where we can have the most positive impact. To learn more about our latest initiatives and priorities, please visit our website to read our 2023 ESG update.

我们将环境、社会和治理（ESG）的工作重点放在能够产生最积极影响的地方。要了解更多有关我们最新举措和优先事项的信息，请访问我们的网站阅读2023年ESG更新。

Forward-Looking Statements

前瞻性声明

Except for historical information, the matters discussed in this press release are forward-looking statements that involve risks and uncertainties. Words like “believe,” “expect,” and “anticipate” mean that these are our best estimates as of this writing, but there can be no assurances that expected or anticipated results or events will actually take place, so our actual future results could differ significantly from those statements.

除历史信息外，本新闻稿中讨论的事项是涉及风险和不确定性的前瞻性声明。像“相信”、“期望”和“期望”这样的词意味着这些是我们在撰写本文时的最佳估计，但不能保证预期或预期的结果或事件会实际发生，因此我们未来的实际结果可能与这些陈述有很大差异。

Factors that could cause or contribute to such differences include, but are not limited to: our ability to successfully integrate the Pro-ficiency business with our own, as well as expenses we may incur in connection therewith, the efficiency and effectiveness of our internal business restructuring and leadership changes, our ability to maintain our competitive advantages, acceptance of new software and improved versions of our existing software by our customers, the general economics of the pharmaceutical industry, our ability to finance growth, our ability to continue to attract and retain highly qualified technical staff, market conditions, macroeconomic factors, and a sustainable market.

可能导致或促成这种差异的因素包括但不限于：我们成功将高效业务与我们自己的业务整合的能力，以及我们可能产生的相关费用，我们内部业务重组和领导层变化的效率和有效性，我们保持竞争优势的能力，客户对新软件和现有软件改进版本的接受程度，制药行业的一般经济状况，我们为增长融资的能力，我们继续吸引和留住高素质技术人员的能力，市场条件，宏观经济因素以及可持续的市场。

Further information on our risk factors is contained in our quarterly and annual reports and filed with the U.S. Securities and Exchange Commission..

有关我们风险因素的更多信息，请参阅我们的季度和年度报告，并提交给美国证券交易委员会。。