There are several critical challenges that can affect bioavailability, including solubility, permeability, drug-drug interactions, and metabolic profile. Traditional methods for addressing these challenges are often time-consuming and expensive, but luckily, Artificial intelligence (AI) can act as a powerful tool to overcome these challenges and improve drug bioavailability ..read more

VeriSIM Life recently announced the availability of AtlasGEN™ Novel Drug Designer, which is the first and only platform that combines generative chemical discovery with biological validation to compress drug candidate selection and reduce costly experimental research dramatically. AtlasGEN is different because it constrains the generative AI with biological data, ensuring relevant and accurate results. Instead of returning 1000 potential molecular hits, most of which are not good, AtlasGEN reduces wasted time, energy and money by returning only the results that are viable ..read more

This article explores the significance of metabolites in drug development and how artificial intelligence (AI) is revolutionizing metabolite identification, leading to improved drug development outcomes. We will delve into the role of metabolites in drug development, the challenges of metabolite identification, and the benefits of AI-powered metabolite identification ..read more

The following transcript was taken from a recorded conversation between Dr. Jo Varshney, CEO of VeriSIM Life, and Dr. Grazia Rovelli, Senior Researcher and Project Leader, Italfarmaco Group. Read on to understand how established companies are approaching AI and integrating it into their strategy as well as their research and development workflows ..read more

The development of a drug is a complex and lengthy process, requiring precise decisions about dosing to ensure both the efficacy and safety of a drug. Artificial intelligence (AI) can be used for dose selection in drug development—helping to save time, money, and increase accuracy. Before embarking upon this journey, it is important to grasp not only the basics of dose selection and why it’s important in drug development, but also how exactly AI improves the dose selection process ..read more

Let’s explore how AI-driven approaches to drug development can be used to decode DDIs, why it is important to detect them, as well as how to integrate AI into your strategy of detecting potential DDIs ..read more

There are many possible roadblocks on the path to IND approval using traditional methods of drug discovery and development alone. Adverse drug-drug interactions (DDIs), ineffective drug formulation, and poorly understood patient stratification are among the top. Hurdles like these can represent “blind alleys” invisible when a candidate is initially discovered, that make carrying forward translational research and clinical trials extremely risky. Luckily, the integration of artificial intelligence (AI) into drug development can help in these three core aspects of de-risking R&D decisions an ..read more

Hardly a week passes without the press spotlighting the growing attention governments are paying to artificial intelligence (AI). For the pharmaceutical and biotech industries, this has created some confusion. Should the progress made by scientists to integrate AI technology as another method to advance exploration and discovery be reconsidered? Are regulators now more concerned about research supported by AI and ML? Will my investigational new drug application be slowed or rejected if submitted with AI-based research? Read the blog to learn more.l ..read more

Recently the FDA released a publication, “Using Artificial Intelligence & Machine Learning in the Development of Drug and Biological Products,” and requested comments on the framework outlined within it from subject matter experts in the industry. VeriSIM Life, which provides a computational drug development platform that uses AI and ML to streamline the drug development process, was eager to contribute our thoughts and feedback to the conversation ..read more

COMMENTARY - Applications of Machine Learning and AI to Drug Discovery, Development, and Regulations. Originally published in The AAPS Journal (2023) 25:70 ..read more