Venturing into the immensity of the small molecule universe to identify novel chemical structure is a much discussed objective of many methods proposed by the chemoinformatics community. To this end, numerous approaches using techniques from the fields of computational de novo design, virtual screening and reaction informatics, among others, have been proposed. Although in principle this objective is commendable, in practice there are several obstacles to useful exploitation of the chemical space. Prime among them are the sheer number of theoretically feasible compounds and the practical concern regarding the synthesizability of the chemical structures conceived using in silico methods. We present the Proximal Lilly Collection initiative implemented at Eli Lilly and Co. with the aims to (i) define the chemical space of small, drug-like compounds that could be synthesized using in-house resources and (ii) facilitate access to compounds in this large space for the purposes of ongoing drug discovery efforts. The implementation of PLC relies on coupling access to available synthetic knowledge and resources with chemo/reaction informatics techniques and tools developed for this purpose. We describe in detail the computational framework supporting this initiative and elaborate on the characteristics of the PLC virtual collection of compounds. As an example of the opportunities provided to drug discovery researchers by easy access to a large, realistically feasible virtual collection such as the PLC, we describe a recent application of the technology that led to the discovery of selective kinase inhibitors.

Increasing the success rate and throughput of drug discovery will require efficiency improvements throughout the process that is currently used in the pharmaceutical community, including the crucial step of identifying hit compounds to act as drivers for subsequent optimization. Hit identification can be carried out through large compound collection screening and often involves the generation and testing of many hypotheses based on available knowledge. In practice, hypothesis generation can involve the selection of promising chemical structures from compound collections using predictive models built from previous screening/ assay results. Available physical collections, typically used during hit identification, are of the order of 10 6 compounds but represent only a small fraction of the small molecule drug-like chemical space. In an effort to survey a larger portion of chemical space and eliminate inefficiencies during hit identification, we introduce a new process, termed Idea2Data (I2D) that tightly integrates computational and experimental components of the drug discovery process. I2D provides the ability to connect a vast virtual collection of compounds readily synthesizable on automated synthesis systems with computational predictive models for the identification of promising structures. This new paradigm enables researchers to process billions of virtual molecules and select structures that can be prepared on automated systems and made available for biological testing, allowing for timely hypothesis testing and follow-up. Since its introduction, I2D has positively impacted several portfolio efforts through identification of new chemical scaffolds and functionalization of existing scaffolds. In this Innovations paper, we describe the I2D process and present an application for the discovery of new ULK inhibitors.

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Tyrosinase is the key enzyme involved in the human pigmentation process, as well as the undesired browning of fruits and vegetables. Compounds inhibiting tyrosinase catalytic activity are an important class of cosmetic and dermatological agents which show high potential as depigmentation agents used for skin lightening. The multi-step protocol employed for the identification of novel tyrosinase inhibitors incorporated the Shape Signatures computational algorithm for rapid screening of chemical libraries. This algorithm converts the size and shape of a molecule, as well its surface charge distribution and other bio-relevant properties, into compact histograms (signatures) that lend themselves to rapid comparison between molecules. Shape Signatures excels at scaffold hopping across different chemical families, which enables identification of new actives whose molecular structure is distinct from other known actives. Using this approach, we identified a novel class of depigmentation agents that demonstrated promise for skin lightening product development.

With the emergence and rapid spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Delta and Omicron variants, escaping vaccine-induced immunity is a concern. Three vaccination schedules, homologous or heterologous, have been initially applied due to an insufficient supply of vaccines in Korea. We investigated neutralizing activities against Omicron and Delta variants in each schedule. Three schedules using three doses of the BNT162b2 (BNT) or the ChAdOx1 (ChAd) vaccines include ChAd-ChAd-BNT, ChAd-BNT-BNT, and BNT-BNT-BNT. Neutralizing activities were evaluated using plaque-reduction neutralization test (PRNT) against wild type (WT) SARS-CoV-2, Delta variant, and Omicron variant. A total of 170 sera from 75 participants were tested, and the baseline characteristics of participants were not significantly different between groups. After the 2nd vaccine dose, geometric mean titers of PRNT ND50 against WT, Delta, and Omicron were highest after ChAd-BNT vaccination (2,463, 1,097, and 107) followed by BNT-BNT (2,364, 674, and 38) and ChAd-ChAd (449, 163, and 25). After the 3rd dose of BNT, the increase of PRNT ND50 against WT, Delta, and Omicron was most robust in ChAd-ChAd-BNT (4,632, 988, and 260), while the BNT-BNT-BNT group showed the most augmented neutralizing activity against Delta and Omicron variants (2,315 and 628). ChAd-BNT-BNT showed a slight increase of PRNT ND50 against WT, Delta, and Omicron (2,757, 1,279, and 230) compared to the 2nd dose. The results suggest that a 3rd BNT booster dose induced strengthened neutralizing activity against Delta and Omicron variants. The waning of cross-reactive neutralizing antibodies after the 3rd dose and the need for additional boosting should be further investigated.