Synthon-based ligand discovery in virtual libraries of over 11 billion compounds

Sadybekov, Arman; Sadybekov, Anastasiia; Liu, Yongfeng; Iliopoulos‐Tsoutsouvas, Christos; Huang, Xi‐Ping; Pickett, Julie E.; Houser, Blake; Patel, Nilkanth; Tran, Ngan; Tong, Fei; Zvonok, Nikolai; Jain, Manish Kumar; Savych, Olena; Radchenko, Dmytro S.; Nikas, Spyros P.; Petasis, Nicos A.; Moroz, Yurii S.; Roth, Bryan L.; Makriyannis, Alexandros; Katritch, Vsevolod

doi:10.1038/s41586-021-04220-9

Cited by 311 publications

(319 citation statements)

References 42 publications

Supporting

Mentioning

255

Contrasting

Order By: Relevance

“…The use of deep learning methods has further revolutionized such ultra large screens and has enabled isolation of potent and specific inhibitors by experimentally screening a relatively manageable number of compounds. Similarly, computational approaches such as V-SYNTHES conduct large scale fragment-based drug development, providing another path to discover and optimize potent inhibitors [ 24 ]. However, such computational methods are applicable only for proteins for which structural information is available.…”

Section: Introductionmentioning

confidence: 99%

Assays Used for Discovering Small Molecule Inhibitors of YAP Activity in Cancers

Maity

Gridnev

Misra

2022

Cancers

View full text Add to dashboard Cite

YAP/TAZ are transcriptional coactivators that function as the key downstream effectors of Hippo signaling. They are commonly misregulated in most human cancers, which exhibit a higher level of expression and nuclear localization of YAP/TAZ, and display addiction to YAP-dependent transcription. In the nucleus, these coactivators associate with TEA domain transcription factors (TEAD1-4) to regulate the expression of genes that promote cell proliferation and inhibit cell death. Together, this results in an excessive growth of the cancerous tissue. Further, YAP/TAZ play a critical role in tumor metastasis and chemotherapy resistance by promoting cancer stem cell fate. Furthermore, they affect tumor immunity by promoting the expression of PD-L1. Thus, YAP plays an important role in multiple aspects of cancer biology and thus, provides a critical target for cancer therapy. Here we discuss various assays that are used for conducting high-throughput screens of small molecule libraries for hit identification, and subsequent hit validation for successful discovery of potent inhibitors of YAP-transcriptional activity. Furthermore, we describe the advantages and limitations of these assays.

show abstract

Section: Introductionmentioning

confidence: 99%

Assays Used for Discovering Small Molecule Inhibitors of YAP Activity in Cancers

Maity

Gridnev

Misra

2022

Cancers

View full text Add to dashboard Cite

show abstract

“…Flowchart of in silico drug discovery with V-SYNTHES, as compared to traditional HTS-driven approach. Figure created using biorender.com A new solution recently developed in our lab 14 helps to resolve this scaling bottleneck by taking advantage of the modular nature of REAL Space libraries (Figure 1). The method, called Virtual SYNThone Hierarchical Enumeration Screening, or V-SYNTHES, first docks and screens a library of chemical fragments, representing all building blocks (synthons) and chemical reaction scaffolds of REAL.…”

Section: F I G U R Ementioning

confidence: 99%

“…The V‐SYNTHES technology was applied in this study to discover new chemotypes for cannabinoid receptors (CBRs) antagonists and ROCK1 kinase inhibitors. 14 For cannabinoid antagonists, chemical synthesis and experimental testing of novel compounds predicted by V‐SYNTHES resulted in hit rate as high as 33%, including 14 submicromolar ligands. This dramatically improved over a standard virtual screening of the Enamine REAL diversity subset, which required approximately 100 times more computational resources.…”

mentioning

confidence: 99%

It all clicks together: In silico drug discovery becoming mainstream

Nazarova

Katritch

2022

Clinical & Translational Med

Self Cite

View full text Add to dashboard Cite

In silico, or computer-aided drug design (CADD) has been around for a few decades, experiencing several waves of hype and disillusionment. The idea that computational modelling of chemical compounds binding to and modulating their receptor targets could someday replace tedious and expensive high-throughput screening assays for hit discovery, as well as custom synthesis of hundreds of derivatives for lead optimization, has always been very attractive. Of course, assays and synthesis still would be needed, but in silico predictions would help to dramatically narrow down the number of compounds to make and assay in the test tube. Although there were quite a few success stories along the way, the general economics of computationally driven drug discovery was never made to work at scale in the past.The last couple of years, however, show signs of a tectonic shift toward embracing in silico drug discovery in both academia and industry. 1 Big Pharma and Biotech are expanding their CADD teams, smaller Biotech companies are hiring their first computational chemists, and many new startups include a major computational component as part of their business plans. Moreover, large and small startups are popping up like mushrooms, where business models heavily rely on computational technologies, which is often a combination of advanced molecular modelling with machine learning and artificial intelligence. Is it just a new wave of hype or the CADD technology has matured enough to become a mainstream part of the drug discovery process? There are several reasons to believe that several key components of CADD, especially based on molecular modelling technologies, have recently "clicked together" to make it scientifically and economically viable, even in competition with ever-growing in vitro technologies like DNA-labelled libraries.These key components include (1) greatly improved availability and accessibility of structural information for drug targets. With more than 190,000 protein structures in PDB, 2 structures are available for most clinically relevant targets or at least for their close orthologs. EspeciallyThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

show abstract

“…In silico approaches enable a wider chemical space to be explored, where databases of online virtual libraries can computationally model chemical-target docking, enabling the screening of vast libraries of potential chemotherapeutics prior to synthesis for experimental validation. Recently, it has been reported that virtual libraries of up to 11 billion compounds can be screened [ 84 ]. Generally, the target structure is converted into a representation that can be used by the docking software.…”

Section: Bottom-up Approachesmentioning

confidence: 99%

Next-Generation Molecular Discovery: From Bottom-Up In Vivo and In Vitro Approaches to In Silico Top-Down Approaches for Therapeutics Neogenesis

Kenny

Antaw

Locke

et al. 2022

Life

View full text Add to dashboard Cite

Protein and drug engineering comprises a major part of the medical and research industries, and yet approaches to discovering and understanding therapeutic molecular interactions in biological systems rely on trial and error. The general approach to molecular discovery involves screening large libraries of compounds, proteins, or antibodies, or in vivo antibody generation, which could be considered “bottom-up” approaches to therapeutic discovery. In these bottom-up approaches, a minimal amount is known about the therapeutics at the start of the process, but through meticulous and exhaustive laboratory work, the molecule is characterised in detail. In contrast, the advent of “big data” and access to extensive online databases and machine learning technologies offers promising new avenues to understanding molecular interactions. Artificial intelligence (AI) now has the potential to predict protein structure at an unprecedented accuracy using only the genetic sequence. This predictive approach to characterising molecular structure—when accompanied by high-quality experimental data for model training—has the capacity to invert the process of molecular discovery and characterisation. The process has potential to be transformed into a top-down approach, where new molecules can be designed directly based on the structure of a target and the desired function, rather than performing screening of large libraries of molecular variants. This paper will provide a brief evaluation of bottom-up approaches to discovering and characterising biological molecules and will discuss recent advances towards developing top-down approaches and the prospects of this.

show abstract

Synthon-based ligand discovery in virtual libraries of over 11 billion compounds

Cited by 311 publications

References 42 publications

Assays Used for Discovering Small Molecule Inhibitors of YAP Activity in Cancers

Assays Used for Discovering Small Molecule Inhibitors of YAP Activity in Cancers

It all clicks together: In silico drug discovery becoming mainstream

Next-Generation Molecular Discovery: From Bottom-Up In Vivo and In Vitro Approaches to In Silico Top-Down Approaches for Therapeutics Neogenesis

Contact Info

Product

Resources

About