![]() ![]() ![]() ( 15) More recently, use of 3D convolutional neural networks has shown considerable promise in predicting protein – ligand binding energy ( 16) (drug potency), and ranking models have made considerable progress is drug repurposing. ( 14) Progress in learning from small amounts of data has been achieved using variants of matching networks. Multitask, fully connected neural networks on these same inputs, has been shown to on average outperform more traditional models, ( 11, 12) including XGBoost, ( 13) with performance scaling monotonically with the number of tasks into the thousands. Researchers in industry have shown that expert-engineered features and support vector machines can be used to predict stability in human liver microsomes ( 9, 10) effectively, among other end points. Over the past two decades, machine learning models have begun to emerge in the industry as a more advanced filter or virtual screen. In practice, even today RO 5 is often still used to evaluate emerging preclinical molecules. ( 8) RO 5 places limits on the number of hydrogen bond donors, acceptors, molecular weight, and lipophilicity measures and has been shown to filter out compounds that are likely to exhibit poor ADME properties. ![]() The first widespread use of a heuristic is Lipinski’s rule of five ( RO 5), invented at Pfizer in 1997. This long, laborious search has historically been guided by the intuition of skilled medicinal chemists and biologists, but over the past few decades, heuristics and machine learning have played an increasingly important role in guiding the process. To make it to the clinic, drug discovery practitioners need to optimize for a wide range of molecular properties, ranging from physical properties, such as aqueous solubility to complex biochemical properties, such as blood-brain barrier penetration. Even restricting the search to molecules with a molecular weight of ≤ 500 Da yields a search space of at least 10 50 molecules, virtually all of which have never been synthesized before. Then, the search process for the right therapeutic compound is kicked off, a process akin to finding the perfect chemical key for a tough to crack biological lock, which is conducted through a vast chemical space containing more molecules than atoms in the universe. ( 7) Long before anything reaches the clinic for validation, a potential disease-modulating biological target is discovered and characterized. Drug Discoveryĭevelopment of novel therapeutics for a human disease is a process that can easily consume a decade of research and development, as well as billions of dollars in capital. Recasting CNNs into this domain is of particular interest in drug discovery, as like nearby pixels, nearby atoms are highly related and interact with each other whereas distant atoms usually do not. Geometric Deep Learning aims to solve this by defining primitives that can operate on these unwieldy data structures, primarily by constructing spatial and spectral interpretations of existing architectures ( 6) such as convolutional neural networks (CNNs). Even operations as simple as addition often cannot find natural constructions, for example, the sum of two atoms or two molecules has no meaning. ![]() Understanding data of this structure has been elusive for classical architectures because of a lack of a well-defined coordinate system and vector space structure in non-euclidian domains. Naturally, there is interest in expanding the domain of applicability of these methods to non-euclidian data such as graphs or manifolds, ( 5) which arise in domains such as 3D models in computer graphics, represented as riemannian manifolds, or graphs in molecular machine learning. ( 4) Perhaps the most important property of DNNs is their ability to automatically learn embeddings (features) tabula rasa from the underlying data, aided by vast amounts of compute and more data than any one human domain expert can understand. Today, deep-learning backs the core technology in many applications, such as self-driving cars, ( 2) speech synthesis, ( 3) and machine translation. Deep neural networks (DNNs) are not an entirely new concept as they have existed for ∼ 20 years, ( 1) only recently entering the spotlight due to an abundance of storage and compute as well as optimization advances. ![]()
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