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With the advent of make-on-demand commercial libraries, the number of purchasable compounds available for virtual screening and assay has grown explosively in recent years, with several libraries eclipsing one billion compounds. Today’s screening libraries are larger and more diverse, enabling the discovery of more-potent hit compounds and unlocking new areas of chemical space, represented by new core scaffolds. Applying physics-based in silico screening methods in an exhaustive manner, where every molecule in the library must be enumerated and evaluated independently, is increasingly cost-prohibitive. Here, we introduce a protocol for machine learning-enhanced molecular docking based on active learning to dramatically increase throughput over traditional docking. We leverage a novel selection protocol that strikes a balance between two objectives: (1) identifying the best scoring compounds and (2) exploring a large region of chemical space, demonstrating superior performance compared to a purely greedy approach. Together with automated redocking of the top compounds, this method captures almost all the high scoring scaffolds in the library found by exhaustive docking. This protocol is applied to our recent virtual screening campaigns against the D4 and AMPC targets that produced dozens of highly potent, novel inhibitors, and a blind test against the MT1 target. Our protocol recovers more than 80% of the experimentally confirmed hits with a 14-fold reduction in compute cost, and more than 90% of the hit scaffolds in the top 5% of model predictions, preserving the diversity of the experimentally confirmed hit compounds.

Adipose-derived stem cells (ASCs) can be applied extensively in the clinic because they can be easily isolated and cause less donor-site morbidity; however, their application can be complicated by patient-specific factors, such as age and harvest site. In this study, we systematically evaluated the effects of age on the quantity and quality of human adipose-derived mesenchymal stem cells (hASCs) isolated from excised chest subcutaneous adipose tissue and investigated the underlying molecular mechanism. hASCs were isolated from donors of 3 different age-groups (i.e., child, young adult, and elderly). hASCs are available from individuals across all age-groups and maintain mesenchymal stem cell (MSC) characteristics. However, the increased age of the donors was found to have a significant negative effect on hASCs frequency base on colony-forming unit fibroblasts assay. Moreover, there is a decline in both stromal vascular fraction (SVF) cell yield and the proliferation rate of hASCs with increasing age, although this relationship is not significant. Aging increases cellular senescence, which is manifested as an increase in SA-β-gal-positive cells, increased mitochondrial-specific reactive oxygen species (ROS) production, and the expression of p21 in the elderly. Further, advancing age was found to have a significant negative effect on the adipogenic and osteogenic differentiation potentials of hASCs, particularly at the early and mid-stages of induction, suggesting a slower response to the inducing factors of hASCs from elderly donors. Finally, impaired migration ability was also observed in the elderly group and was determined to be associated with decreased expression of chemokine receptors, such as CXCR4 and CXCR7. Taken together, these results suggest that, while hASCs from different age populations are phenotypically similar, they present major differences at the functional level. When considering potential applications of hASCs in cell-based therapeutic strategies, the negative influence of age on hASC differentiation potential and migration abilities should be taken seriously.

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Enrichment of ligands versus property-matched decoys is widely used to test and optimize docking library screens. However, the unconstrained optimization of enrichment alone can mislead, leading to false confidence in prospective performance. This can arise by over-optimizing for enrichment against property-matched decoys, without considering the full spectrum of molecules to be found in a true large library screen. Adding decoys representing charge extrema helps mitigate over-optimizing for electrostatic interactions. Adding decoys that represent the overall characteristics of the library to be docked allows one to sample molecules not represented by ligands and property-matched decoys but that one will encounter in a prospective screen. An optimized version of the DUD-E set (DUDE-Z), as well as Extrema and sets representing broad features of the library (Goldilocks), is developed here. We also explore the variability that one can encounter in enrichment calculations and how that can temper one's confidence in small enrichment differences. The new tools and new decoy sets are freely available at http://tldr.docking.org and http://dudez.docking.org.

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