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.
Depending on which Terminator movies you watch, the evil artificial intelligence Skynet has either already taken over humanity or is about to do so. But it's not just science fiction writers who are worried about the dangers of uncontrolled AI. In a 2019 survey by Emerj, an AI research and advisory company, 14% of AI researchers said that AI was an "existential threat" to humanity. Even if the AI apocalypse doesn't come to pass, shortchanging AI ethics poses big risks to society -- and to the enterprises that deploy those AI systems. Central to these risks are factors inherent to the technology -- for example, how a particular AI system arrives at a given conclusion, known as its "explainability" -- and those endemic to an enterprise's use of AI, including reliance on biased data sets or deploying AI without adequate governance in place.
Advanced technologies such as artificial intelligence (AI), machine learning (ML), and natural language processing (NLP) have become a cornerstone of successful modern clinical trials, integrated into many of the technologies enabling the transformation of clinical development. The health and life sciences industry's dramatic leap forward into the digital age in recent years has been a game-changer with innovations and scientific breakthroughs that are improving patient outcomes and population health. Consequently, embracing digital transformation is no longer an option but an industry standard. Let's explore what that truly means for clinical development. Over the years, technology has equipped clinical leaders to successfully reduce costs while accelerating stages of research and development.
InstaDeep is an EMEA leader in delivering decision-making AI products. Leveraging their extensive know-how in GPU-accelerated computing, deep learning, and reinforcement learning, they have built products, such as the novel DeepChain platform, to tackle the most complex challenges across a range of industries. InstaDeep has also developed collaborations with global leaders in the AI ecosystem, such as Google DeepMind, NVIDIA, and Intel. They are part of Intel's AI Builders program and are one of only 2 NVIDIA Elite Service Delivery Partners across EMEA. The InstaDeep team is made up of approximately 155 people working across its network of offices in London, Paris, Tunis, Lagos, Dubai, and Cape Town, and is growing fast.
DeepMind, a British company owned by Google, may be on the verge of achieving human-level artificial intelligence (AI). Nando de Freitas, a research scientist at DeepMind and machine learning professor at Oxford University, has said'the game is over' in regards to solving the hardest challenges in the race to achieve artificial general intelligence (AGI). AGI refers to a machine or program that has the ability to understand or learn any intellectual task that a human being can, and do so without training. According to De Freitas, the quest for scientists is now scaling up AI programs, such as with more data and computing power, to create an AGI. Earlier this week, DeepMind unveiled a new AI'agent' called Gato that can complete 604 different tasks'across a wide range of environments'. Gato uses a single neural network – a computing system with interconnected nodes that works like nerve cells in the human brain.
In September 2021, scientists Sean Ekins and Fabio Urbina were working on an experiment they had named the "Dr. The Swiss government's Spiez laboratory had asked them to find out what would happen if their AI drug discovery platform, MegaSyn, fell into the wrong hands. In much the way undergraduate chemistry students play with ball-and-stick model sets to learn how different chemical elements interact to form molecular compounds, Ekins and his team at Collaborations Pharmaceuticals used publicly available databases containing the molecular structures and bioactivity data of millions of molecules to teach MegaSyn how to generate new compounds with pharmaceutical potential. The plan was to use it to accelerate the drug discovery process for rare and neglected diseases. The best drugs are ones with high specificity--acting only on desired or targeted cells or neuroreceptors, for instance--and low toxicity to reduce ill effects.
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Artificial intelligence does all kinds of things….genomics Genetic engineering has always been a go-to plot twist in sci-fi movies and TV shows. The idea of genetically mutated humans with superior abilities and unique DNAs still has ripple effects on Marvel fans and box offices. But what if we can alter genes in real life? CRISPR gene editing has been doing that since 2012 (no Wolverine or Magneto though). In 2022, this powerful genetic engineering technique is complemented with artificial intelligence.
A 3D rendering of a protein complex structures predicted from protein sequences by AF2Complex. From the muscle fibers that move us to the enzymes that replicate our DNA, proteins are the molecular machinery that makes life possible. Protein function heavily depends on their three-dimensional structure, and researchers around the world have long endeavored to answer a seemingly simple inquiry to bridge function and form: if you know the building blocks of these molecular machines, can you predict how they are assembled into their functional shape? This question is not so easy to answer. With complex structures dependent on intricate physical interactions, researchers have turned to artificial neural network models – mathematical frameworks that convert complex patterns into numerical representations – to predict and "see" the shape of proteins in 3D.
Whether we realize it or not, most of us deal with artificial intelligence (AI) every day. Each time you do a Google Search or ask Siri a question, you are using AI. The catch, however, is that the intelligence these tools provide is not really intelligent. They don't truly think or understand in the way humans do. Rather, they analyze massive data sets, looking for patterns and correlations.