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

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.

Virtual discovery of melatonin receptor ligands to modulate circadian rhythms.

The neuromodulator melatonin synchronizes circadian rhythms and related physiological functions through the actions of two G-protein-coupled receptors: MT1 and MT2. Circadian release of melatonin at night from the pineal gland activates melatonin receptors in the suprachiasmatic nucleus of the hypothalamus, synchronizing the physiology and behaviour of animals to the light-dark cycle1-4. The two receptors are established drug targets for aligning circadian phase to this cycle in disorders of sleep5,6 and depression1-4,7-9. Despite their importance, few in vivo active MT1-selective ligands have been reported2,8,10-12, hampering both the understanding of circadian biology and the development of targeted therapeutics. Here we docked more than 150 million virtual molecules to an MT1 crystal structure, prioritizing structural fit and chemical novelty. Of these compounds, 38 high-ranking molecules were synthesized and tested, revealing ligands with potencies ranging from 470 picomolar to 6 micromolar. Structure-based optimization led to two selective MT1 inverse agonists-which were topologically unrelated to previously explored chemotypes-that acted as inverse agonists in a mouse model of circadian re-entrainment. Notably, we found that these MT1-selective inverse agonists advanced the phase of the mouse circadian clock by 1.3-1.5 h when given at subjective dusk, an agonist-like effect that was eliminated in MT1- but not in MT2-knockout mice. This study illustrates the opportunities for modulating melatonin receptor biology through MT1-selective ligands and for the discovery of previously undescribed, in vivo active chemotypes from structure-based screens of diverse, ultralarge libraries.

SAR by Space: Enriching Hit Sets from the Chemical Space.

We introduce SAR-by-Space, a concept to drastically accelerate structure-activity relationship (SAR) elucidation by synthesizing neighboring compounds that originate from vast chemical spaces. The space navigation is accomplished within minutes on affordable standard computer hardware using a tree-based molecule descriptor and dynamic programming. Maximizing the synthetic accessibility of the results from the computer is achieved by applying a careful selection of building blocks in combination with suitably chosen reactions; a decade of in-house quality control shows that this is a crucial part in the process. The REAL Space is the largest chemical space of commercially available compounds, counting 11 billion molecules as of today. It was used to mine actives against bromodomain 4 (BRD4). Before synthesis, compounds were docked into the binding site using a scoring function, which incorporates intrinsic desolvation terms, thus avoiding time-consuming simulations. Five micromolecular hits have been identified and verified within less than six weeks, including the measurement of IC50 values. We conclude that this procedure is a substantial time-saver, accelerating both ligand- and structure-based approaches in hit generation and lead optimization stages.

Pros and cons of virtual screening based on public "Big Data": In silico mining for new bromodomain inhibitors.

The Virtual Screening (VS) study described herein aimed at detecting novel Bromodomain BRD4 binders and relied on knowledge from public databases (ChEMBL, REAXYS) to establish a battery of predictive models of BRD activity for in silico selection of putative ligands. Beyond the actual discovery of new BRD ligands, this represented an opportunity to practically estimate the actual usefulness of public domain "Big Data" for robust predictive model building. Obtained models were used to virtually screen a collection of 2 million compounds from the Enamine company collection. This industrial partner then experimentally screened a subset of 2992 molecules selected by the VS procedure for their high likelihood to be active. Twenty nine confirmed hits were detected after experimental testing, representing 1% of the selected candidates. As a general conclusion, this study emphasizes once more that public structure-activity databases are nowadays key assets in drug discovery. Their usefulness is however limited by the state-of-the-art knowledge harvested so far by published studies. Target-specific structure-activity information is rarely rich enough, and its heterogeneity makes it extremely difficult to exploit in rational drug design. Furthermore, published affinity measures serving to build models selecting compounds to be experimentally screened may not be well correlated with the experimental hit selection criterion (in practice, often imposed by equipment constraints). Nevertheless, a robust 2.6-fold increase in hit rate with respect to an equivalent, random screening campaign showed that machine learning is able to extract some real knowledge in spite of all the noise in structure-activity data.