Drug Discovery Today: Technologies最新文献

Molecular representations encoding molecular structure information play critical roles in molecular virtual screening (VS). In order to improve VS performance, an abundance of molecular encoders have been developed and tested by various VS challenges. Combinational strategies were also used to improve the performance. Deep learning (DL)-based molecular encoders have attracted much attention for their automatic information extraction ability. In this review, we present an overview of two-dimensional-, three-dimensional-, and DL-based molecular encoders, summarize recent progress of VS using DL technologies, and propose a general framework of DL molecular encoder-based VS. Perspectives on the future directions of molecular representations and applications in the prediction of active compounds are also provided.

This review provides an overview of descriptions of atoms applied to the understanding of phenomena like chemical reactivity and selectivity, pK_a values, Site of Metabolism prediction, or hydrogen bond strengths, but also the substitution of quantum mechanical calculations by machine learning models for energies, forces or even spectrosocopic properties and finally the fast calculation of atomic charges for force field parametrization. The descriptor space ranges from derivatives of the wavefunctions or electron density via quantum mechanics derived descriptors to classical descriptions of atoms and their embedding in a molecule. The common denominator for all approaches is the thorough understanding of the physics of the chemical problem that guided the design of the atom descriptor. Quantum mechanics (QM) and machine learning (ML) finally are converging to a new discipline, namely QM/ML.

The discovery of new chemical entities is a crucial part of drug discovery, which requires the lead compounds to have desired properties to be pharmaceutically active. De novo drug design aims to generate and optimize novel ligands for macromolecular targets from scratch. The development of graph-based deep generative neural networks has provided a new method. In this review, we gave a brief introduction to graph representation and graph-based generative models for de novo drug design, summarized them as four architectures, and concluded each’s characteristics. We also discussed generative models for scaffold- and fragment-based design and graph-based generative models’ future directions.

Machine learning and artificial intelligence (ML/AI) have become important research tools in molecular medicine and chemistry. Their rise and recent success in drug discovery promises a rapid progression of development pipelines while reshaping how fundamental and clinical research is conducted. By taking advantage of the ever-growing wealth of publicly available and proprietary data, learning algorithms now provide an attractive means to generate statistically motivated research hypotheses. Hitherto unknown data patterns may guide and prioritize experiments, and augment expert intuition. Therefore, data is a key component in the model building workflow. Herein, I aim to discuss types of chemical and biological data according to their quality and reemphasize general recommendations for their use in ML/AI.

The large number of monogenic metabolic disorders originating in the liver poses a unique opportunity for development of gene therapy modalities to pursue curative approaches. Various disorders have been successfully treated via liver-directed gene therapy, though most of the advances have been in animal models, with only limited success in clinical trials. Pre-clinical data in animals using non-viral approaches, including the Sleeping Beauty transposon system, are discussed. The various advances with viral vectors for liver-directed gene therapy are also a focus of this review, including retroviral, adenoviral, recombinant adeno-associated viral, and SV40 vectors. Genome editing techniques, including zinc finger nucleases, transcription activator-like effector nucleases and clustered regularly interspaced short palindromic repeats (CRISPR), are also described. Further, the various controversies in the field with regards to somatic vs. germline editing using CRISPR in humans are explored, while also highlighting the myriad of preclinical advances. Lastly, newer technologies are reviewed, including base editing and prime editing, which use CRISPR with exciting adjunctive properties to avoid double-stranded breaks and thus the recruitment of endogenous repair mechanisms. While encouraging results have been achieved recently, there are still significant challenges to overcome prior to the broad use of vector-based and genome editing techniques in the clinical arena. As these technologies mature, the promise of a cure for many disabling inherited metabolic disorders is within reach, and urgently needed.

Driven by rapid advances in computer hardware and publicly available datasets over the past decade, deep learning has achieved tremendous success in the transformation of many computational disciplines. These novel technologies have had considerable impact on computer-aided drug design as well, throughout all stages of the development pipeline. A flexible toolbox of neural architectures has been developed that are well-suited to represent the sequential, topological, or geometrical concepts of chemistry and biology; and that are able to either discriminate existing molecules or to generate new ones from scratch. For some biochemical prediction tasks, the state of the art has been advanced; however, for complex and practically relevant projects, the outcomes are less clear-cut. Current deep learning methods rely on massive amounts of labeled examples, but drug discovery data is comparatively limited in quantity and quality. These problems need to be resolved and existing sources used more effectively to demonstrate that deep learning can revolutionize the field in general.

The Ubiquitin/Proteasome System comprises an essential cellular mechanism for regulated protein degradation. Ubiquitination may also promote the assembly of protein complexes that initiate intracellular signaling cascades. Thus, proper regulation of substrate protein ubiquitination is essential for maintaining normal cellular physiology. Deubiquitinases are the class of enzymes responsible for removing ubiquitin modifications from target proteins and have been implicated in regulating human disease. As such, deubiquitinases are now recognized as emerging drug targets. Small molecule deubiquitinase inhibitors have been developed; among those, inhibitors for the deubiquitinases USP7 and USP14 are the best-characterized given that they are structurally validated. In this review we discuss the normal physiological roles of the USP7 and USP14 deubiquitinases as well as the pathological conditions associated with their dysfunction, with a focus on oncology and neurodegenerative diseases. We also review structural biology of USP7 and USP14 enzymes and the characterization of their respective inhibitors, highlighting the various molecular mechanisms by which these deubiquitinases may be functionally inhibited. Finally, we summarize the cellular and in vivo studies performed using the structurally-validated USP7 and USP14 inhibitors.