Simulations Plus and the University of Southern California Secure NIH Grant to Develop New AI Drug Discovery Offerings
Simulations Plus and the University of Southern California have secured an NIH grant to develop new AI drug discovery offerings. The partnership aims to advance ligand-based virtual screening by incorporating water-ligand interactions into drug design and optimization activities.
The project will integrate USC's WATGEN algorithm for predicting water positions with Simulations Plus's ADMET Predictor platform. Machine learning approaches will be applied to predict pharmacophore features for 3D shape and feature matching. The goal is to create a first-of-its-kind LBVS solution that enhances current methods and accelerates the discovery of more effective drugs.
The team will validate the technology by designing drugs against defined targets, synthesizing and testing selected compounds experimentally. This collaboration between industry and academia aims to reduce the Design-Make-Test-Analyze cycle of drug discovery significantly.
Simulations Plus e l'Università della California del Sud hanno ottenuto un finanziamento NIH per sviluppare nuove offerte di scoperta farmaceutica basate su IA. La partnership mira a migliorare lo screening virtuale basato su ligandi integrando le interazioni acqua-ligando nelle attività di design e ottimizzazione dei farmaci.
Il progetto integrerà l'algoritmo WATGEN di USC per la previsione delle posizioni dell'acqua con la piattaforma ADMET Predictor di Simulations Plus. Saranno applicati approcci di machine learning per prevedere le caratteristiche del farmacoforo per il matching della forma 3D e delle caratteristiche. L'obiettivo è creare una soluzione LBVS unica nel suo genere che migliori i metodi attuali e acceleri la scoperta di farmaci più efficaci.
Il team convaliderà la tecnologia progettando farmaci contro obiettivi definiti, sintetizzando e testando sperimentalmente i composti selezionati. Questa collaborazione tra industria e accademia mira a ridurre significativamente il ciclo Design-Fabbricazione-Test-Analisi della scoperta di farmaci.
Simulations Plus y la Universidad del Sur de California han asegurado una para desarrollar nuevas ofertas de descubrimiento de fármacos mediante IA. La asociación tiene como objetivo avanzar en el cribado virtual basado en ligandos incorporando interacciones agua-ligando en el diseño y optimización de medicamentos.
El proyecto integrará el algoritmo WATGEN de USC para predecir posiciones del agua con la plataforma ADMET Predictor de Simulations Plus. Se aplicarán enfoques de aprendizaje automático para predecir características farmacóforas para el emparejamiento de forma y características en 3D. El objetivo es crear una solución LBVS sin precedentes que mejore los métodos actuales y acelere el descubrimiento de fármacos más efectivos.
El equipo validará la tecnología diseñando fármacos contra objetivos definidos, sintetizando y probando experimentalmente los compuestos seleccionados. Esta colaboración entre la industria y la academia tiene como objetivo reducir significativamente el ciclo de Diseño-Fabricación-Prueba-Análisis en el descubrimiento de fármacos.
Simulations Plus와 남부 캘리포니아 대학교가 NIH 보조금을 확보하여 새로운 AI 약물 발견 솔루션을 개발하고 있습니다. 이 파트너십은 리간드 기반 가상 스크리닝을 발전시키고 물-리간드 상호작용을 약물 설계 및 최적화 활동에 통합하는 것을 목표로 합니다.
이 프로젝트는 USC의 WATGEN 알고리즘을 물의 위치 예측에 통합하고 Simulations Plus의 ADMET Predictor 플랫폼과 결합됩니다. 기계 학습 방법을 적용하여 3D 형태 및 특징 매칭을 위한 약리학적 특성을 예측할 것입니다. 목표는 현재 방법을 개선하고 더 효과적인 약물 발견을 가속화하는 유일무이한 LBVS 솔루션을 만드는 것입니다.
팀은 정해진 표적에 대해 약물을 설계하고, 선택된 화합물을 합성 및 실험적으로 테스트하여 기술을 검증할 것입니다. 이 산업과 학계 간의 협력은 약물 발견의 설계-제작-테스트-분석 주기를 상당히 단축하는 것을 목표로 합니다.
Simulations Plus et l'Université de Californie du Sud ont obtenu une bourse du NIH pour développer de nouvelles offres de découverte de médicaments basées sur l'IA. Le partenariat vise à faire progresser le criblage virtuel basé sur les ligands en intégrant les interactions eau-ligand dans les activités de conception et d'optimisation des médicaments.
Le projet intégrera l'algorithme WATGEN de l'USC pour prédire les positions de l'eau avec la plateforme ADMET Predictor de Simulations Plus. Des approches d'apprentissage automatique seront appliquées pour prédire les caractéristiques pharmacophoriques pour l'appariement de formes et de caractéristiques en 3D. L'objectif est de créer une solution LBVS sans précédent qui améliore les méthodes actuelles et accélère la découverte de médicaments plus efficaces.
L'équipe validera la technologie en concevant des médicaments ciblant des cibles définies, en synthétisant et en testant expérimentalement les composés sélectionnés. Cette collaboration entre l'industrie et le monde académique vise à réduire considérablement le cycle Conception-Fabrication-Test-Analyse de la découverte de médicaments.
Simulations Plus und die Universität Südkalifornien haben einen NIH-Zuschuss erhalten, um neue KI-basierte Arzneimittelentdeckungslösungen zu entwickeln. Die Partnerschaft hat zum Ziel, das ligandbasierte virtuelle Screening voranzutreiben, indem Wasser-Ligand-Interaktionen in die Arzneimittelgestaltung und -optimierung integriert werden.
Das Projekt wird den WATGEN-Algorithmus von USC zur Vorhersage von Wasserpositionen mit der ADMET Predictor-Plattform von Simulations Plus integrieren. Maschinelles Lernen wird angewendet, um pharmazeutische Eigenschaften für die 3D-Form- und Merkmalsanpassung vorherzusagen. Das Ziel ist es, eine einzigartige LBVS-Lösung zu schaffen, die aktuelle Methoden verbessert und die Entdeckung effektiverer Arzneimittel beschleunigt.
Das Team wird die Technologie validieren, indem es Arzneimittel gegen definierte Ziele entwirft, ausgewählte Verbindungen synthetisiert und experimentell testet. Diese Zusammenarbeit zwischen Industrie und Wissenschaft zielt darauf ab, den Design-Herstellen-Test-Analyse-Zyklus der Arzneimittelentdeckung erheblich zu verkürzen.
- Secured NIH grant for developing new AI drug discovery offerings
- Partnership with University of Southern California to advance ligand-based virtual screening
- Integration of USC's WATGEN algorithm with Simulations Plus's ADMET Predictor platform
- Potential to create a first-of-its-kind LBVS solution for pharmaceutical companies
- Aim to reduce the Design-Make-Test-Analyze cycle of drug discovery
- None.
Insights
This NIH grant to Simulations Plus and USC represents a significant advancement in drug discovery technology. The project aims to develop a novel ligand-based virtual screening (LBVS) solution that incorporates water-ligand interactions, a important factor often overlooked in existing software. This approach could potentially accelerate drug discovery and improve the efficiency of identifying promising drug candidates.
The integration of machine learning with validated 3D-based calculations is particularly noteworthy. By combining these advanced computational methods with Simulations Plus' ADMET Predictor platform, the company is positioning itself at the forefront of AI-driven drug design. This could lead to increased market share and revenue growth in the competitive biosimulation and drug discovery software market.
While the immediate financial impact may be , the long-term potential is substantial. Successful development and commercialization of this technology could attract more pharmaceutical clients and research partnerships, potentially boosting Simulations Plus' revenue and market position in the coming years. Investors should monitor the progress of this project and its integration into the company's product offerings.
The collaboration between Simulations Plus and USC to develop AI-driven drug discovery tools represents a significant leap in the application of artificial intelligence to pharmaceutical research. By incorporating water-ligand interactions into ligand-based virtual screening, this project addresses a critical gap in current drug design methodologies.
The use of machine learning to predict pharmacophore features and integrate them with 3D shape matching algorithms is particularly innovative. This approach has the potential to dramatically improve the accuracy and efficiency of early-stage drug discovery, potentially reducing the time and cost associated with identifying promising drug candidates.
From an AI perspective, this project demonstrates the growing trend of combining domain-specific knowledge (in this case, pharmaceutical science) with advanced machine learning techniques. The success of this approach could pave the way for similar AI-driven innovations in other areas of scientific research and drug development, potentially creating new market opportunities for Simulations Plus in the rapidly evolving field of AI-assisted drug discovery.
Partnership will advance the field of ligand-based virtual screening to improve drug design and optimization activities
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.
“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. 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.”
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.”
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.
“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. 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.”
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.
About the Haworth laboratory in the USC Mann School at the University of
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. 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.
About 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. 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. 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
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