Journal: Expert Opinion on Drug Discovery

Abbreviation

Expert Opin. Drug Discov.

Publisher

Taylor & Francis

Journal Volumes

ISSN

1746-0441
1746-045X

Description

Filters

2017 - 201922020 - 20254
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Publications 1 - 6 of 6
  • Finding a needle in the haystack: ADME and pharmacokinetics/pharmacodynamics characterization and optimization towards orally available bifunctional protein degraders
    Item type: Review Article
    Apprato, Giulia; Caron, Giulia; Deshmukh, Gauri; et al. (2025)
    Expert Opinion on Drug Discovery
    Introduction: Degraders are an increasingly important sub-modality of small molecules as illustrated by an ever-expanding number of publications and clinical candidate molecules in human trials. Nevertheless, their preclinical optimization of ADME and PK/PD properties has remained challenging. Significant research efforts are being directed to elucidate the underlying principles and to derive rational optimization strategies. Areas covered: In this review, the authors summarize currently best practices in terms of in vitro assays and in vivo experiments. Furthermore, the authors collate and comment on the current understanding of optimal physicochemical characteristics and their impact on absorption, distribution, metabolism, and excretion properties, including the current knowledge of drug-rug interactions. Finally, the authors describe the pharmacokinetic prediction and Pharmacokinetic/Pharmacodynamic -concepts unique to degraders and how to best implement these in research projects. Expert opinion: Despite many recent advances in the field, continued research will further our understanding of rational design regarding degrader optimization. Machine-learning and computational approaches will become increasingly important once larger, more robust datasets become available. Furthermore, tissue-targeting approaches (particularly regarding the central nervous system will be increasingly studied to elucidate efficacious drug regimens that capitalize on the catalytic mode of action. Finally, additional specialized approaches (e.g. covalent degraders, LOVdegs) can further enrich the field and offer interesting alternative approaches.
  • Artificial intelligence in drug discovery: recent advances and future perspectives
    Item type: Review Article
    Jiménez-Luna, José; Grisoni, Francesca; Weskamp, Nils; et al. (2021)
    Expert Opinion on Drug Discovery
    Introduction: Artificial intelligence (AI) has inspired computer-aided drug discovery. The widespread adoption of machine learning, in particular deep learning, in multiple scientific disciplines, and the advances in computing hardware and software, among other factors, continue to fuel this development. Much of the initial skepticism regarding applications of AI in pharmaceutical discovery has started to vanish, consequently benefitting medicinal chemistry. Areas covered: The current status of AI in chemoinformatics is reviewed. The topics discussed herein include quantitative structure-activity/property relationship and structure-based modeling, de novo molecular design, and chemical synthesis prediction. Advantages and limitations of current deep learning applications are highlighted, together with a perspective on next-generation AI for drug discovery. Expert opinion: Deep learning-based approaches have only begun to address some fundamental problems in drug discovery. Certain methodological advances, such as message-passing models, spatial-symmetry-preserving networks, hybrid de novo design, and other innovative machine learning paradigms, will likely become commonplace and help address some of the most challenging questions. Open data sharing and model development will play a central role in the advancement of drug discovery with AI.
  • Macromolecular target prediction by self-organizing feature maps
    Item type: Review Article
    Schneider, Gisbert; Schneider, Petra (2017)
    Expert Opinion on Drug Discovery
  • Editorial. Advancing drug discovery via GPU-based deep learning
    Item type: Other Journal Item
    Gawehn, Erik; Hiss, Jan A.; Brown, John B.; et al. (2018)
    Expert Opinion on Drug Discovery
  • High-throughput screening in multicellular spheroids for target discovery in the tumor microenvironment
    Item type: Review Article
    Calpe, Blaise; Kovacs, Werner J. (2020)
    Expert Opinion on Drug Discovery
    ntroduction: Solid tumors are highly influenced by a complex tumor microenvironment (TME) that cannot be modeled with conventional two-dimensional (2D) cell culture. In addition, monolayer culture conditions tend to induce undesirable molecular and phenotypic cellular changes. The discrepancy between in vitro and in vivo is an important factor accounting for the high failure rate in drug development. Three-dimensional (3D) multicellular tumor spheroids (MTS) more closely resemble the in vivo situation in avascularized tumors. Areas covered: This review describes the use of MTS for anti-cancer drug discovery, with an emphasis on high-throughput screening (HTS) compatible assays. In particular, we focus on how these assays can be used for target discovery in the context of the TME. Expert opinion: Arrayed MTS in microtiter plates are HTS compatible but remain more expensive and time consuming than their 2D culture counterpart. It is therefore imperative to use assays with multiplexed readouts, in order to maximize the information that can be gained with the screen. In this context, high-content screening allowing to uncover microenvironmental dependencies is the true added value of MTS-based screening compared to 2D culture-based screening. Hit translation in animal models will, however, be key to allow a broader use of MTS-based screening in industry. © 2020 Informa UK Limited, trading as Taylor & Francis Group.
  • An insight into artificial intelligence in drug discovery: an interview with Professor Gisbert Schneider
    Item type: Other Journal Item
    Schneider, Gisbert (2021)
    Expert Opinion on Drug Discovery
Publications 1 - 6 of 6