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AI-designed COVID-19 drug nominated for preclinical trials

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Updated An oral medication designed by scientists with the help of AI algorithms could one day treat patients with COVID-19 and other types of diseases caused by coronaviruses. Insilico Medicine, a biotech startup based in New York, announced on Tuesday it had nominated a drug candidate for preclinical trials – the stage before you start testing it on humans. Today's mRNA vaccines boost the body's immunity to COVID-19 by aiding the generation of antibodies capable of blocking the virus's spike protein, stopping the bio-nasty from infecting cells. The small molecule developed by Insilico, however, is used to treat people already infected, and works by preventing the coronavirus from replicating. The preclinical candidate has a specialized structure to target the 3C-like (3CL) protease, an enzyme involved in the viral reproduction of the SARS-CoV-2 coronavirus, Feng Ren, Insilico's chief scientific officer, explained.


Artificial intelligence makes a splash in small-molecule drug discovery

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In the past five years, interest in applying artificial intelligence (AI) approaches in drug research and development (R&D) has surged. Driven by the expectation of accelerated timelines, reduced costs and the potential to reveal hidden insights from vast datasets, more than 150 companies with a focus on AI have raised funding in this period, based on an analysis of the field by Back Bay Life Science Advisors (Figure 1a). And the number of financings and average amount raised soared in 2021. At the forefront of this field are companies harnessing AI approaches such as machine learning (ML) in small-molecule drug discovery, which account for the majority of financings backed by venture capital (VC) in recent years (Figure 1b), as well as some initial public offerings (IPOs) for pioneers in the area (Table 1). Such companies have also attracted large pharma companies to establish multiple high-value partnerships (Table 2), and the first AI-based small-molecule drug candidates are now in clinical trials (Nat.


Artificial Intelligence at the Heart of China's New Drug Discovery

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It's not much of a surprise to find Artificial Intelligence (AI) playing a central role in the pharmaceutical industry. Chinese firms are relying on AI to put more drugs on the market, and by extrapolation extend better services. The country is gathering momentum for an artificial intelligence-backed drug discovery boom. All thanks to the nation's emphasis on innovation-driven development, these companies are going through a continuously improving innovation ecosystem, according to industry experts and business leaders. "It is not a question of whether China will become a powerhouse in AI-driven drug development even though it started relatively late (in the field). The only question is when that will happen." said an Industry Leader in AI-based Drug Discovery.


Early Development Medicinal Chemistry: Utilizing Data and Artificial Intelligence

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In early development of medicinal chemistry, there are a lot of considerations, such as determining promising agents and dosage form. Pharmaceutical Technology interviewed Chase Smith, PhD, senior application scientist at Optibrium (a software company for drug discovery), and Kevin Short, director of medicinal chemistry at Verseon International (a clinical-stage pharmaceutical company), who discuss key considerations for medicinal agents in early development, challenges and opportunities in medicinal chemistry, what data to consider when selecting a high-potential drug candidate, and how artificial intelligence (AI) can be harnessed in this process. PharmTech: What are key considerations when working with medicinal agents in the early development phase? Short (Verseon): The most obvious general consideration is whether or not there are multiple paths forward. Since the medicinal chemist will inevitably synthesize multiple rounds of compounds in order to optimize physicochemical properties, pharmacologists will need to ensure there are easily accessible and relevant pharmacokinetics and disease models, which will interrogate the compound candidates.


Israeli team says AI platform can predict which drugs are safe

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Robert Langer, the co-founder of Moderna and a lauded MIT professor, said, "We are at the tipping point of the modernization of drug discovery" and that the "Quris platform could be a significant value to pharma companies and the health of society at large." Langer is a member of the scientific advisory board of Quris, which officially launched this week and announced $9 million in seed funding to support its efforts. Nobel laureate Aaron Ciechanover is the chairman of the company's scientific advisory board. Quris, based in Israel and Boston, is an artificial intelligence (AI) company operating in the pharmaceutical space. Its team has developed an AI platform to predict which drug candidates will work most safely and effectively in humans.


How a portfolio approach to AI helps your ROI

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Instead of computing the success or failure of AI initiatives on a project-by-project basis, companies using the portfolio approach compute the ROI for all their AI initiatives. A portfolio approach works in other areas of business, and the same principles apply here. Take a look at three relevant examples and the lessons for AI. In the pharmaceutical world, developing a new drug takes an average of at least ten years and costs over $2.6 billion. Literally thousands and even millions of molecules and investigative drugs are studied during the initial drug discovery and preclinical trial phases of the R&D process.


Auransa and POLARISqb enter research collaboration finding treatments for neglected women's diseases

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Auransa Inc., an artificial intelligence (AI) company developing precision medicines in areas of unmet medical needs, and Polaris Quantum Biotech (POLARISqb), a quantum drug design company, announced a research collaboration addressing therapeutics for neglected diseases disproportionately affecting women. The partnership seeks to discover treatments that may tackle many such diseases, and their complementary expertise promises to seek solutions that elude medical research. Auransa is an AI-driven biotech company, with a pipeline of novel compounds for various diseases. Auransa's proprietary predictive computational platform, SMarTR Engine, uses computational approaches to tackle disease heterogeneity to predict targets and compounds, generating insights from molecular data. POLARISqb built the first drug discovery platform using quantum computing, making the process ten times faster.


First Alzheimer's disease drug candidate designed by AI enters Phase I clinical trials - Pharmafield

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The world's first Alzheimer's disease (AD) drug candidate designed by artificial intelligence (AI) is entering Phase I clinical trials, thanks to a successful collaboration between Exscientia Ltd and Sumitomo Dainippon Pharma. In the announcement from Exscientia, it states that they will initiate a Phase 1 clinical study of DSP-0038 in the United States for the treatment of Alzheimer's disease psychosis. DSP-0038 is the third molecule created using Exscientia's Artificial Intelligence (AI) technologies to enter clinical trials. The two earlier compounds are DSP-1181, announced in 2020 together with Sumitomo Dainippon Pharma to treat obsessive-compulsive disorder, and Exscientia's immuno-oncology agent, EXS-21546, announced earlier this year. Joint research between Exscientia and Sumitomo Dainippon Pharma designed DSP-0038 to be a single small molecule that exhibits high potency as an antagonist for the 5-HT2A receptor and agonist for the 5-HT1A receptor, whilst selectively avoiding similar receptors and unwanted targets, such as the dopamine D2 receptor.


Valence Discovery: transforming AI-enabled drug design

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Artificial intelligence (AI) has become an increasingly popular tool for drug companies discovering and designing new therapies. According to analysis by Deloitte, the AI market in drug discovery is expected to grow from $159.8m in 2018 to $2.9bn by 2025. Of the almost 180 start-ups involved in AI-assisted drug discovery in 2019, 40% were working on repurposing existing drugs or generating novel drug candidates using AI, machine learning, and automation. AI-enabled drug design company Valence Discovery, formerly InVivo AI, was founded in 2018. Since its rebrand last month, the company has announced a series of impressive drug discovery and design partnerships, with the aim of making advanced technology accessible to R&D organisations of all sizes.


Toxicity Detection in Drug Candidates using Simplified Molecular-Input Line-Entry System

arXiv.org Artificial Intelligence

The need for analysis of toxicity in new drug candidates and the requirement of doing it fast have asked the consideration of scientists towards the use of artificial intelligence tools to examine toxicity levels and to develop models to a degree where they can be used commercially to measure toxicity levels efficiently in upcoming drugs. Artificial Intelligence based models can be used to predict the toxic nature of a chemical using Quantitative Structure Activity Relationship techniques. Convolutional Neural Network models have demonstrated great outcomes in predicting the qualitative analysis of chemicals in order to determine the toxicity. This paper goes for the study of Simplified Molecular Input Line-Entry System (SMILES) as a parameter to develop Long short term memory (LSTM) based models in order to examine the toxicity of a molecule and the degree to which the need can be fulfilled for practical use alongside its future outlooks for the purpose of real world applications.