AutoDesigner - Core Design, a De Novo Design Algorithm for Chemical Scaffolds: Application to the Design and Synthesis of Novel Selective Wee1 Inhibitors.

The hit identification stage of a drug discovery program generally involves the design of novel chemical scaffolds with desired biological activity against the target(s) of interest. One common approach is scaffold hopping, which is the manual design of novel scaffolds based on known chemical matter. One major limitation of this approach is narrow chemical space exploration, which can lead to difficulties in maintaining or improving biological activity, selectivity, and favorable property space. Another limitation is the lack of preliminary structure-activity relationship (SAR) data around these designs, which could lead to selecting suboptimal scaffolds to advance lead optimization. To address these limitations, we propose AutoDesigner - Core Design (CoreDesign), a de novo scaffold design algorithm. Our approach is a cloud-integrated, de novo design algorithm for systematically exploring and refining chemical scaffolds against biological targets of interest. The algorithm designs, evaluates, and optimizes a vast range, from millions to billions, of molecules in silico, following defined project parameters encompassing structural novelty, physicochemical attributes, potency, and selectivity using active-learning FEP. To validate CoreDesign in a real-world drug discovery setting, we applied it to the design of novel, potent Wee1 inhibitors with improved selectivity over PLK1. Starting from a single known ligand and receptor structure, CoreDesign rapidly explored over 23 billion molecules to identify 1,342 novel chemical series with a mean of 4 compounds per scaffold. To rapidly analyze this large amount of data and prioritize chemical scaffolds for synthesis, we utilize t-Distributed Stochastic Neighbor Embedding (t-SNE) plots of in silico properties. The chemical space projections allowed us to rapidly identify a structurally novel 5-5 fused core meeting all the hit-identification requirements. Several compounds were synthesized and assayed from the scaffold, displaying good potency against Wee1 and excellent PLK1 selectivity. Our results suggest that CoreDesign can significantly speed up the hit-identification process and increase the probability of success of drug discovery campaigns by allowing teams to bring forward high-quality chemical scaffolds derisked by the availability of preliminary SAR.

AutoDesigner, a De Novo Design Algorithm for Rapidly Exploring Large Chemical Space for Lead Optimization: Application to the Design and Synthesis of d-Amino Acid Oxidase Inhibitors.

The lead optimization stage of a drug discovery program generally involves the design, synthesis, and assaying of hundreds to thousands of compounds. The design phase is usually carried out via traditional medicinal chemistry approaches and/or structure-based drug design (SBDD) when suitable structural information is available. Two of the major limitations of this approach are (1) difficulty in rapidly designing potent molecules that adhere to myriad project criteria, or the multiparameter optimization (MPO) problem, and (2) the relatively small number of molecules explored compared to the vast size of chemical space. To address these limitations, we have developed AutoDesigner, a de novo design algorithm. AutoDesigner employs a cloud-native, multistage search algorithm to carry out successive rounds of chemical space exploration and filtering. Millions to billions of virtual molecules are explored and optimized while adhering to a customizable set of project criteria such as physicochemical properties and potency. Additionally, the algorithm only requires a single ligand with measurable affinity and a putative binding model as a starting point, making it amenable to the early stages of an SBDD project where limited data are available. To assess the effectiveness of AutoDesigner, we applied it to the design of novel inhibitors of d-amino acid oxidase (DAO), a target for the treatment of schizophrenia. AutoDesigner was able to generate and efficiently explore over 1 billion molecules to successfully address a variety of project goals. The compounds generated by AutoDesigner that were synthesized and assayed (1) simultaneously met not only physicochemical criteria, clearance, and central nervous system (CNS) penetration (Kp,uu) cutoffs but also potency thresholds and (2) fully utilize structural data to discover and explore novel interactions and a previously unexplored subpocket in the DAO active site. The reported data demonstrate that AutoDesigner can play a key role in accelerating the discovery of novel, potent chemical matter within the constraints of a given drug discovery lead optimization campaign.

Massive Theoretical Screen of Hole Conducting Organic Materials in the Heteroacene Family by Using a Cloud-Computing Environment.

Materials exhibiting higher mobilities than conventional organic semiconducting materials such as fullerenes and fused thiophenes are in high demand for applications in printed electronics. To discover new molecules in the heteroacene family that might show improved charge mobility, a massive theoretical screen of hole conducting properties of molecules was performed by using a cloud-computing environment. Over 7 000 000 structures of fused furans, thiophenes and selenophenes were generated and 250 000 structures were randomly selected to perform density functional theory (DFT) calculations of hole reorganization energies. The lowest hole reorganization energy calculated was 0.0548 eV for a fused thioacene having 8 aromatics rings. Hole mobilities of compounds with the lowest 130 reorganization energy were further processed by applying combined DFT and molecular dynamics (MD) methods. The highest mobility calculated was 1.02 and 9.65 cm2/(V s) based on percolation and disorder theory, respectively, for compounds containing selenium atoms with 8 aromatic rings. These values are about 20 times higher than those for dinaphthothienothiophene (DNTT).

Reliable and Accurate Solution to the Induced Fit Docking Problem for Protein-Ligand Binding.

We present a reliable and accurate solution to the induced fit docking problem for protein-ligand binding by combining ligand-based pharmacophore docking, rigid receptor docking, and protein structure prediction with explicit solvent molecular dynamics simulations. This novel methodology in detailed retrospective and prospective testing succeeded to determine protein-ligand binding modes with a root-mean-square deviation within 2.5 Å in over 90% of cross-docking cases. We further demonstrate these predicted ligand-receptor structures were sufficiently accurate to prospectively enable predictive structure-based drug discovery for challenging targets, substantially expanding the domain of applicability for such methods.