Drug Discovery Today: Technologies最新文献

Compared to traditional vaccines that are formulated into mixtures of an adjuvant and an antigen, a self-adjuvanting vaccine consists of an antigen that is covalently conjugated to a well-defined adjuvant. In self-adjuvanting vaccines, innate immune receptor ligands are usually used as adjuvants. Innate immune receptor ligands effectively trigger acquired immunity through the activation of innate immunity to enhance host immune responses to antigens. When a self-adjuvanting vaccine is used, immune cells simultaneously uptake the antigen and the adjuvant because they are covalently linked. Consequently, the adjuvant can specifically induce immune responses against the conjugated antigen. Importantly, self-adjuvanting vaccines do not require co-administration of additional adjuvants or immobilization to carrier proteins, which enables avoidance of the use of highly toxic adjuvants or the induction of undesired immune responses. Given these excellent properties, self-adjuvanting vaccines are expected to serve as candidates for the next generation of vaccines. Herein, we review vaccine adjuvants, with a focus on the adjuvants used in self-adjuvanting vaccines, and then overview recent advances made with self-adjuvanting conjugate vaccines.

A detailed understanding of the interactions between drugs and their targets is crucial to develop the best possible therapeutic agents. Structure-based drug design relies on the availability of high-resolution structures obtained primarily through X-ray crystallography. Collecting and analysing quickly a large quantity of structural data is crucial to accelerate drug discovery pipelines. Researchers from academia and industry can access the highly automated macromolecular crystallography (MX) beamlines of Diamond Light Source, the UK national synchrotron, to rapidly collect diffraction data from large numbers of crystals. With seven beamlines dedicated to MX, Diamond offers bespoke solutions for a wide variety of user requirements. Working in synergy with state-of-the-art laboratories and other life science instruments to provide an integrated offering, the MX beamlines provide innovative and multidisciplinary approaches to the determination of structures of new pharmacological targets as well as the efficient study of protein-ligand complexes.

Microbial surface polysaccharides are important virulence factors and targets for vaccine development. Glycoconjugate vaccines, obtained by covalently linking carbohydrates and proteins, are well established tools for prevention of bacterial infections. Elucidation of the minimal portion involved in the interactions with functional antibodies is of utmost importance for the understanding of their mechanism of induction of protective immune responses and the design of synthetic glycan based vaccines. Typically, this is achieved by combination of different techniques, which include ELISA, glycoarray, Surface Plasmon Resonance in conjunction with approaches for mapping at atomic level the position involved in binding, such as Saturation Transfer NMR and X-ray crystallography. This review provides an overview of the structural studies performed to map glycan epitopes (glycotopes), with focus on the highly complex structure of Group B Streptococcus type III (GBSIII) capsular polysaccharide. Furthermore, it describes the rational process followed to translate the obtained information into the design of a protective glycoconjugate vaccine based on a well-defined synthetic glycan epitope.

There has been a wave of generative models for molecules triggered by advances in the field of Deep Learning. These generative models are often used to optimize chemical compounds towards particular properties or a desired biological activity. The evaluation of generative models remains challenging and suggested performance metrics or scoring functions often do not cover all relevant aspects of drug design projects. In this work, we highlight some unintended failure modes in molecular generation and optimization and how these evade detection by current performance metrics.

This is a review article of Generative Topographic Mapping (GTM) – a non-linear dimensionality reduction technique producing generative 2D maps of high-dimensional vector spaces – and its specific applications in Drug Design (chemical space cartography, compound library design and analysis, virtual screening, pharmacological profiling, de novo drug design, conformational space & docking interaction cartography, etc.) Written by chemoinformaticians for potential users among medicinal chemists and biologists, the article purposely avoids all underlying mathematics. First, the GTM concept is intuitively explained, based on the strong analogies with the rather popular Self-Organizing Maps (SOMs), which are well established library analysis tools. GTM is basically a fuzzy-logics-based generalization of SOMs. The second part of the review, some of published GTM applications in drug design are briefly revisited.

Artificial intelligence (AI) has become a powerful tool in many fields, including drug discovery. Among various AI applications, molecular property prediction can have more significant immediate impact to the drug discovery process since most algorithms and methods use predicted properties to evaluate, select, and generate molecules. Herein, we provide a brief review of the state-of-art molecular property prediction methodologies and discuss examples reported recently. We highlight key techniques that have been applied to molecular property prediction such as learned representation, multi-task learning, transfer learning, and federated learning. We also point out some critical but less discussed issues such as data set quality, benchmark, model performance evaluation, and prediction confidence quantification.

Active machine learning enables the automated selection of the most valuable next experiments to improve predictive modelling and hasten active retrieval in drug discovery. Although a long established theoretical concept and introduced to drug discovery approximately 15 years ago, the deployment of active learning technology in the discovery pipelines across academia and industry remains slow. With the recent re-discovered enthusiasm for artificial intelligence as well as improved flexibility of laboratory automation, active learning is expected to surge and become a key technology for molecular optimizations. This review recapitulates key findings from previous active learning studies to highlight the challenges and opportunities of applying adaptive machine learning to drug discovery. Specifically, considerations regarding implementation, infrastructural integration, and expected benefits are discussed. By focusing on these practical aspects of active learning, this review aims at providing insights for scientists planning to implement active learning workflows in their discovery pipelines.

Interest in docking technologies has grown parallel to the ever increasing number and diversity of 3D models for macromolecular therapeutic targets. Structure-Based Virtual Screening (SBVS) aims at leveraging these experimental structures to discover the necessary starting points for the drug discovery process. It is now established that Machine Learning (ML) can strongly enhance the predictive accuracy of scoring functions for SBVS by exploiting large datasets from targets, molecules and their associations. However, with greater choice, the question of which ML-based scoring function is the most suitable for prospective use on a given target has gained importance. Here we analyse two approaches to select an existing scoring function for the target along with a third approach consisting in generating a scoring function tailored to the target. These analyses required discussing the limitations of popular SBVS benchmarks, the alternatives to benchmark scoring functions for SBVS and how to generate them or use them using freely-available software.

Application of AI technologies in synthesis prediction has developed very rapidly in recent years. We attempt here to give a comprehensive summary on the latest advancement on retro-synthesis planning, forward synthesis prediction as well as quantum chemistry-based reaction prediction models. Besides an introduction on the AI/ML models for addressing various synthesis related problems, the sources of the reaction datasets used in model building is also covered. In addition to the predictive models, the robotics based high throughput experimentation technology will be another crucial factor for conducting synthesis in an automated fashion. Some state-of-the-art of high throughput experimentation practices carried out in the pharmaceutical industry are highlighted in this chapter to give the reader a sense of how future chemistry will be conducted to make compounds faster and cheaper.

Proteochemometrics is a machine learning based modeling approach relying on a combination of ligand and protein descriptors. With ongoing developments in machine learning and increases in public data the technique is more frequently applied in early drug discovery, typically in ligand–target binding prediction. Common applications include improvements to single target quantitative structure-activity relationship models, protein selectivity and promiscuity modeling, and large-scale deep learning approaches. The increase in predictive power using proteochemometrics is observed in multi-target bioactivity modeling, opening the door to more extensive studies covering whole protein families. On top of that, with deep learning fueling more complex and larger scale models, proteochemometrics allows faster and higher quality computational models supporting the design, make, test cycle.