Artificial Intelligence for Drug Discovery, Biomarker Development, and Generation of Novel Chemistry.

and Generation of Novel Chemistry T productivity of the pharmaceutical industry is on the decline. Failure rates in clinical trials exceed 90% after therapies are tested in model organisms, and the cost to develop a new drug exceeds $2.6 billion. Recent advances in artificial intelligence (AI) may help to reverse this trend and accelerate and improve pharmaceutical R&D. While the term AI and the concept of deep learning are not new, recent advances in high-performance computing, the availability of large annotated data sets required for training, and new frameworks for implementing deep neural networks (DNNs) resulted in an unprecedented acceleration of the field. Since 2014, DNNs have surpassed human accuracy in image, voice and text recognition, autonomous driving, and many other tasks. Early presentations to the pharmaceutical industry on the advances in deep learning in 2014 and 2015 resulted in skepticism and were discarded. In 2017, many pharmaceutical companies started partnering with AI startups and academics or started internal R&D programs. From training DNNs on transcriptional response data for predicting the pharmacological properties of small molecules and biomarker development, to the generation of novel chemistry, deep learning techniques rapidly propagated into many areas of biomedical research. The body of knowledge and the range of applications of deep learning and other machine learning techniques has expanded quickly and permeated into many areas of drug discovery. There have been hundreds of publications deposited in peer-reviewed journals and on ArXiv. In June 2017, Molecular Pharmaceutics announced a special issue titled “Deep Learning for Drug Discovery and Biomarker Development” focused on the applications of AI in chemistry and biomedicine (Figure 1). After a call to the most prominent scientists publishing on deep learning in the areas of computational chemistry and biology, 10 research papers were accepted. One of the main opportunities for AI in drug discovery is in drug repurposing using abundant data sets available from highthroughput experiments with gene expression profiles. Specifically, transcriptional response profiles generated by the Broad Institute, such as the Connectivity Map. The connectivity map uses gene expression signatures to connect small molecules, genes, and disease available through the LINCS Project. Donner et al. used the L1000 data set to develop a new method for measuring the compound functional similarity based on gene expression data for drug repurposing. The method identified drugs with shared therapeutic and biological targets even when the compounds were structurally dissimilar, thereby revealing previously unreported functional relationships between compounds. Imaging data is among the most abundant data type available for deep learning researchers, often allowing for rapid validation of results using human visual sensory organs. Many of the tools developed for image recognition and trained and tested on simple pictures are now available for researchers working with more complex imaging data types, including computed tomography. Gao and Qian used the patch-based convolutional neural network (CNN) model combined with support vector machines to predict multidrug resistant patients with tuberculosis using a data set of 230 patients from the ImageCLEF2017 competition, achieving reasonably high classification rates. Xiang and colleagues presented a multitask deep autoencoder for the prediction of the human cytochrome 450 inhibition, laying the roadmap for reducing the side effects associated with inhibiting the CYP450. Lane et al. compared various machine learning models for predicting hit molecules forMycobacterium tuberculosis (Mtb) using a small curated data set of molecules targeting Mtb. Another article by the Ekins group compared various machine learning techniques for predicting the estrogen receptor (ER) binding. In this work, Russo and co-authors compared the AdaBoost, Bernoulli Naiv̈e-Bayes, Random Forest, support vector classification, and deep neural networks using a variety of metrics and a proprietary data set compiled from public sources to predict ER binding. Again, Random Forest outperformed the other algorithms, demonstrating the value of comparing the various algorithms, especially for simple machine learning tasks. One of the many chemistry related machine learning challenges is the selection of the representation of molecular structure to capture as many of the relevant chemical and biological features and come as close to reality as possible. There are many representations of molecular structures, including a variety of molecular fingerprints, string-based representations, molecular graphs, and others. A molecular graph is a popular representation of the molecular structure for machine learning applications and explored by many groups working on medicinal chemistry related tasks. Hop and colleagues explored the performance of geometric deep learning methods in the context of drug discovery, comparing machine learned features against the domain expert engineered features. The CNN graph outperformed the methods trained on expert engineered features on most of the data sets. The popular deep learning techniques involving CNN are often trained on 2D and 3D images. To help facilitate f the many applications of CNNs in chemistry, Kuzminykh presented the wave transform-based representation of the 3D molecular structure. The group demonstrated that the proposed representation leads to the better performance of CNNbased autoencoders than either the voxel-based representation or the previously used Gaussian blur of atoms, and it can be successfully applied to classification tasks, such as MACCS fingerprint prediction. Deep generative models, commonly referred to as AI imagination, enabled many new applications requiring creativity and