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

Structure-based virtual ligand screening is emerging as a key paradigm for early drug discovery owing to the availability of high-resolution target structures1-4 and ultra-large libraries of virtual compounds5,6. However, to keep pace with the rapid growth of virtual libraries, such as readily available for synthesis (REAL) combinatorial libraries7, new approaches to compound screening are needed8,9. Here we introduce a modular synthon-based approach-V-SYNTHES-to perform hierarchical structure-based screening of a REAL Space library of more than 11 billion compounds. V-SYNTHES first identifies the best scaffold-synthon combinations as seeds suitable for further growth, and then iteratively elaborates these seeds to select complete molecules with the best docking scores. This hierarchical combinatorial approach enables the rapid detection of the best-scoring compounds in the gigascale chemical space while performing docking of only a small fraction (<0.1%) of the library compounds. Chemical synthesis and experimental testing of novel cannabinoid antagonists predicted by V-SYNTHES demonstrated a 33% hit rate, including 14 submicromolar ligands, substantially improving over a standard virtual screening of the Enamine REAL diversity subset, which required approximately 100 times more computational resources. Synthesis of selected analogues of the best hits further improved potencies and affinities (best inhibitory constant (Ki) = 0.9 nM) and CB2/CB1 selectivity (50-200-fold). V-SYNTHES was also tested on a kinase target, ROCK1, further supporting its use for lead discovery. The approach is easily scalable for the rapid growth of combinatorial libraries and potentially adaptable to any docking algorithm.

Generative and reinforcement learning approaches for the automated de novo design of bioactive compounds.

Deep generative neural networks have been used increasingly in computational chemistry for de novo design of molecules with desired properties. Many deep learning approaches employ reinforcement learning for optimizing the target properties of the generated molecules. However, the success of this approach is often hampered by the problem of sparse rewards as the majority of the generated molecules are expectedly predicted as inactives. We propose several technical innovations to address this problem and improve the balance between exploration and exploitation modes in reinforcement learning. In a proof-of-concept study, we demonstrate the application of the deep generative recurrent neural network architecture enhanced by several proposed technical tricks to design inhibitors of the epidermal growth factor (EGFR) and further experimentally validate their potency. The proposed technical solutions are expected to substantially improve the success rate of finding novel bioactive compounds for specific biological targets using generative and reinforcement learning approaches.

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.

One-Pot Parallel Synthesis of 5-(Dialkylamino)tetrazoles.

Two protocols for the combinatorial synthesis of 5-(dialkylamino)tetrazoles were developed. The best success rate (67%) was shown by the method that used primary and secondary amines, 2,2,2-trifluoroethylthiocarbamate, and sodium azide as the starting reagents. The key steps included the formation of unsymmetrical thiourea, subsequent alkylation with 1,3-propane sultone and cyclization with azide anion. A 559-member aminotetrazole library was synthesized by this approach; the overall readily accessible (REAL) chemical space covered by the method exceeded 7 million feasible compounds.

Evolution of commercially available compounds for HTS.

Over recent years, an industry of compound suppliers has grown to provide drug discovery with screening compounds: it is estimated that there are over 16 million compounds available from these sources. Here, we review the chemical space covered by suppliers' compound libraries (SCL) in terms of compound physicochemical properties, novelty, diversity, and quality. We examine the feasibility of compiling high-quality vendor-based libraries avoiding complicated, expensive compound management activity, and compare the resulting libraries to the ChEMBL data set. We also consider how vendors have responded to the evolving requirements for drug discovery.

Facile One-Pot Parallel Synthesis of 3-Amino-1,2,4-triazoles.

A 1,2,4-triazole motif is present in numerous commercialized and investigational bioactive molecules. Despite its importance for medicinal chemistry, there is a lack of convenient combinatorial approaches toward this molecular core. Herein, we present a synthetic strategy suitable for the quick preparation of a library of structurally diverse 1,2,4-triazoles in a one-pot setting. The key steps include the formation of thioureas followed by S-alkylation using 1,3-propane sultone and consecutive ring closure leading to the desired 1,2,4-triazoles. Parallel synthesis yields thousands of 1,2,4-triazoles in a cost- and time-efficient manner from commercially available chemicals.

An Old Story in the Parallel Synthesis World: An Approach to Hydantoin Libraries.

An approach to the parallel synthesis of hydantoin libraries by reaction of in situ generated 2,2,2-trifluoroethylcarbamates and α-amino esters was developed. To demonstrate utility of the method, a library of 1158 hydantoins designed according to the lead-likeness criteria (MW 200-350, cLogP 1-3) was prepared. The success rate of the method was analyzed as a function of physicochemical parameters of the products, and it was found that the method can be considered as a tool for lead-oriented synthesis. A hydantoin-bearing submicromolar primary hit acting as an Aurora kinase A inhibitor was discovered with a combination of rational design, parallel synthesis using the procedures developed, in silico and in vitro screenings.