An open-source drug discovery platform enables ultra-large virtual screens

By Christoph Gorgulla, A. Boeszoermenyi, Zi-Fu Wang, P. Fischer, Paul W. Coote, Krishna M. Padmanabha Das, Yehor S. Malets, D. Radchenko, Yurii S. Moroz, David A. Scott and 5 more. Nature, 2020

519 citations recorded by the source corpus.

On average, an approved drug currently costs US$2–3 billion and takes more than 10 years to develop1. In part, this is due to expensive and time-consuming wet-laboratory experiments, poor initial hit compounds and the high attrition rates in the (pre-)clinical phases. Structure-based virtual screening has the potential to mitigate these problems. With structure-based virtual screening, the quality of the hits improves with the number of compounds screened2. However, despite the fact that large databases of compounds exist, the ability to carry out large-scale structure-based virtual screening on computer clusters in an accessible, efficient and flexible manner has remained difficult. Here we describe VirtualFlow, a highly automated and versatile open-source platform with perfect scaling behaviour that is able to prepare and efficiently screen ultra-large libraries of compounds. VirtualFlow is able to use a variety of the most powerful docking programs. Using VirtualFlow, we prepared one of the largest and freely available ready-to-dock ligand libraries, with more than 1.4 billion commercially available molecules. To demonstrate the power of VirtualFlow, we screened more than 1 billion compounds and identified a set of structurally diverse molecules that bind to KEAP1 with submicromolar affinity. One of the lead inhibitors (iKeap1) engages KEAP1 with nanomolar affinity (dissociation constant (Kd) = 114 nM) and disrupts the interaction between KEAP1 and the transcription factor NRF2. This illustrates the potential of VirtualFlow to access vast regions of the chemical space and identify molecules that bind with high affinity to target proteins. VirtualFlow, an open-source drug discovery platform, enables the efficient preparation and virtual screening of ultra-large ligand libraries to identify molecules that bind with high affinity to target proteins.

Subfields: Biophysics, Computational Theory and Mathematics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/b63c0ca8e8e79a323f1e9a55ff8ccbd110f63422

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Citations in the corpus

A geometric deep learning approach to predict binding conformations of bioactive molecules (2021, methodology): “Each residual block starts by projecting the node and edge features (vh−1 i and e h−1 i,j , respectively) to a 32D vector using an MLP as shown in equations (5) and (6).”
Synthon-based ligand discovery in virtual libraries of over 11 billion compounds (2021, methodology): “Two binding modes for the CB 2 receptor binding pocket were prepared: one guided by 20 known antagonists and another by 20 agonists, selected from ChEMBL high-affinity ligands for CB 2 (CHEMBL253, affinity p Kd > 8).”
Computational approaches streamlining drug discovery (2023, methodology): “The practical utility of the REAL database has been recently demonstrated in several major prospective screening campaigns 20,21,23,24 , some of them taking further hit optimization steps in the same chemical space, yielding selective nanomolar and even sub-nanomolar ligands without any custom…”