Hit identification and optimization in virtual screening: practical recommendations based on a critical literature analysis

Abstract

A critical analysis of virtual screening results published between 2007 and 2011 was performed. The activity of reported hit compounds from over 400 studies was compared to their hit identification criteria. Hit rates and ligand efficiencies were calculated to assist in these analyses, and the results were compared with factors such as the size of the virtual library and the number of compounds tested. A series of promiscuity, druglike, and ADMET filters were applied to the reported hits to assess the quality of compounds reported, and a careful analysis of a subset of the studies that presented hit optimization was performed. These data allowed us to make several practical recommendations with respect to selection of compounds for experimental testing, definition of hit identification criteria, and general virtual screening hit criteria to allow for realistic hit optimization. A key recommendation is the use of size-targeted ligand efficiency values as hit identification criteria.

Figure 1. Hit cutoff ranges

Values in blue were obtained from studies with clearly defined hit cutoffs; values in red are estimated from the lowest experimental activity reported for a hit.

Figure 2. Ligand efficiency analysis of the most active reported VS hits

(A) LE sorted by virtual screening study, (B) LE plotted against HAC for each compound reported.

Figure 3. Flagged problematic compounds

(A) Shown are representative structures of potentially problematic compounds from initial hits (colored as black) and next generation compounds (colored as blue), identified by the application of the REOS, PAINS, and Eli Lilly filters. (B) Venn diagram showing the overlap of potentially problematic compounds from the different filters against initial hits.

Figure 4. Summary of ADMET property predictions of initial hits and next generation compounds by QikProp

Number of stars: Number of properties or descriptors that fall outside the 95% range of similar values for known drugs. (Recommended acceptable range is 0–5.)

Figure 5. Ligand efficiency and potency improvement during optimization

(A) LE improvements during optimization was monitored by ratio changes as following: LE(ratio) = LE(after optimization)/LE(before optimization); (B) Potency improvements during optimization was monitored by Potency(ratio) = Potency(before optimization)/Potency(after optimization).

Figure 6. Ligand efficiency as a hit identification metric

(A) Expected relationship between Log (IC₅₀) and percentage inhibition based on four-parameter logistic Hill equation at 10, 25, 50 and 100 μM screening concentrations. (B) Adjusted target LE values at different heavy atom count according to Equation (2). (C) Estimated percentage inhibition values at different HAC for hit selection cut-offs in order to maintain LE at target values shown in (B).