Bayer’s in silico ADMET platform: a journey of machine learning over the past two decades

Göller, Andreas H.; Kuhnke, Lara; Montanari, Floriane; Bonin, Anne; Schneckener, Sebastian; Laak, Antonius ter; Wichard, Jörg D.; Lobell, Mario; Hillisch, Alexander

doi:10.1016/j.drudis.2020.07.001

Cited by 147 publications

(85 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Given that the composition of the screened compound collection is governed by drug‐like molecules, the ligandability becomes an indicator for the druggability. For the assessment of druglikeness of identified hits, we have an in‐silico absorption, distribution, metabolism, excretion and toxicity (ADMET) platform [9] in place. Overall, the observed hit rate roughly correlates with the chance to extract a suitable chemical series for optimization (Table 1) as can be seen for FIIa, FXa and FXIa on the one hand compared to FIXa and FVIIa on the other hand.…”

Section: Resultsmentioning

confidence: 99%

Druggability Assessment for Selected Serine Proteases in a Pharmaceutical Industry Setting

et al. 2020

Self Cite

View full text Add to dashboard Cite

Target druggability assessment is an integral part of the early target characterization and selection process in pharmaceutical industry. Here, we investigate a set of five different serine proteases from the blood coagulation cascade. The aim of this study is twofold. Firstly, leveraging the wealth of available in‐house high‐throughput screening (HTS) data, we analyze HTS hit rates and discuss their predictive value for the development of small molecule (SMOL) candidates. Purely structure‐activity relationship (SAR) based druggability ratings are compared with computational protein‐structure based druggability assessments. Secondly, we evaluate the impact of using conformational ensembles from molecular dynamics (MD) simulations instead of single static crystal structures as basis for computational druggability assessments. Based on this study, we recommend incorporating molecular dynamics routinely into the early target characterization process, especially if only a single X‐ray structure is available.

show abstract

Section: Resultsmentioning

confidence: 99%

Druggability Assessment for Selected Serine Proteases in a Pharmaceutical Industry Setting

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…The impact of these computational models on drug discovery is undeniable, evidenced by the successful prediction of biological activity and pharmacokinetic parameters, viz. absorption, distribution, metabolism, excretion, and toxicity (ADMET) [17][18][19][20][21]. For ligand-based QSAR/QSPR modeling, the structural features of molecules (e. g. as pharmacophore distribution, physicochemical properties, and functional groups) are commonly converted into machine-readable numbers using the so-called molecular descriptors [7].…”

Section: Qsar/qspr and Structure-based Modeling With Artificial Intelligencementioning

confidence: 99%

Artificial intelligence in drug discovery: recent advances and future perspectives

Jiménez-Luna

Grisoni

Weskamp

et al. 2021

Expert Opinion on Drug Discovery

248

122

View full text Add to dashboard Cite

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.

show abstract

“…As the volume and chemotype coverage of the available ADME‐T databases are continually growing, we have witnessed a great progress in AI/ML‐guided ADME‐T prediction in recent years. Such advances in the field have been extensively reviewed 136,227–232 …”

Section: Ai/ml Applications In Drug Discoverymentioning

confidence: 99%

Artificial intelligence and machine learning‐aided drug discovery in central nervous system diseases: State‐of‐the‐arts and future directions

Vatansever

Schlessinger

Wacker

et al. 2020

Medicinal Research Reviews

217

View full text Add to dashboard Cite

Neurological disorders significantly outnumber diseases in other therapeutic areas. However, developing drugs for central nervous system (CNS) disorders remains the most challenging area in drug discovery, accompanied with the long timelines and high attrition rates. With the rapid growth of biomedical data enabled by advanced experimental technologies, artificial intelligence (AI) and machine learning (ML) have emerged as an indispensable tool to draw meaningful insights and improve decision making in drug discovery. Thanks to the advancements in AI and ML algorithms, now the AI/ML‐driven solutions have an unprecedented potential to accelerate the process of CNS drug discovery with better success rate. In this review, we comprehensively summarize AI/ML‐powered pharmaceutical discovery efforts and their implementations in the CNS area. After introducing the AI/ML models as well as the conceptualization and data preparation, we outline the applications of AI/ML technologies to several key procedures in drug discovery, including target identification, compound screening, hit/lead generation and optimization, drug response and synergy prediction, de novo drug design, and drug repurposing. We review the current state‐of‐the‐art of AI/ML‐guided CNS drug discovery, focusing on blood–brain barrier permeability prediction and implementation into therapeutic discovery for neurological diseases. Finally, we discuss the major challenges and limitations of current approaches and possible future directions that may provide resolutions to these difficulties.

show abstract

Bayer’s in silico ADMET platform: a journey of machine learning over the past two decades

Cited by 147 publications

References 53 publications

Druggability Assessment for Selected Serine Proteases in a Pharmaceutical Industry Setting

Druggability Assessment for Selected Serine Proteases in a Pharmaceutical Industry Setting

Artificial intelligence in drug discovery: recent advances and future perspectives

Artificial intelligence and machine learning‐aided drug discovery in central nervous system diseases: State‐of‐the‐arts and future directions

Contact Info

Product

Resources

About