Scoring

This article provides insights into a computer-based profiling tool implemented in Poland from 2014 to 2019 to measure the employability of unemployed individuals and decide upon allocation of active labor market policies. We propose to treat the profiling tool as a source of information about what was expected from the unemployed citizens by state authorities and which attitudes were perceived by the state as “desirable” or “demanding adjustment.” We show how the profiling technology served to shape the conduct of the unemployed population, and how it imposed upon them a certain ideal of social citizenship. Our findings indicate that the normative assumptions underlying the profiling relate directly to the key aspects of welfare state transformations, namely, to the new social contract which delegitimizes financial benefits and puts forward activation, to the new concept of a citizen as an entrepreneurial and self-reliant actor and to the new, individualized perception of social risks. Results are based on the analysis of the profiling questionnaire, scoring mechanism as well as the reconstruction of the policy-making process.

Current virtual screening tools are fast, but reliable scoring is elusive. Here, we present the 'SQM/COSMO filter', a novel scoring function featuring a quantitative semiempirical quantum mechanical (SQM) description of all types of noncovalent interactions coupled with implicit COSMO solvation. We show unequivocally that it outperforms eight widely used scoring functions. The accuracy and chemical generality of the SQM/COSMO filter make it a perfect tool for late stages of virtual screening. Despite the enormous advances in method development for structure-based in silico drug design, reliable predictions of the structures (docking) and affinities (scoring) of protein–ligand (P–L) complexes still remain an unsolved task. 1 A plethora of scoring functions (SFs) have been devised by utilising experimental data for regression analyses, by constructing knowledge-based potentials, or based on physical laws. 2,3 As none of the SFs is general enough to perform equally strongly for a diverse set of P–L complexes, utilising several SFs at once (consensus scoring) holds promise. 4 Regression analysis and knowledge-based approaches to scoring are trained on a set of P–L complexes and rely on variable master equation terms. Their validity is limited to complexes similar to the training set. In principle, this problem has been overcome in physics-based methods. Because of computational cost, preference has been given to molecular mechanics (MM) methods, such as the combination of MM interaction energies with implicit solvation free energy terms (generalised Born, GB, or Poisson–Boltzmann, PB) to estimate affinities. 2 Additionally, the wide coverage of organic chemical space in the GAFF (general AMBER force field) 5 has made the parameterisation of ligands for MM straightforward. However, an explicit description of quantum mechanical (QM) effects in P–L interactions, such as charge transfer, polarisa-tion, covalent-bond formation or s-hole bonding, was missing. QM methods, which describe these effects qualitatively better than the energy functions used in MM-based SFs, were thus introduced into computational drug design. 6,7 Recent developments in QM methods and algorithms as well as the availability of a powerful computing infrastructure have paved the way to apply them for P–L complexes in numerous setups: linear scaling or efficient parallelisation of semi-empirical QM (SQM) methods, 7–10 QM/MM, 7,8,11,12 DFT-D3 on truncated P–L complexes 13 or various fragmentation methods. 11,14 Specifically, AM1, RM1, PM6 or DF–TB SQM methods have been used 7–9,12,15 as such or with empirical corrections for dispersion, hydrogen-and halogen-bonding 16 to describe the P–L noncovalent interactions. Merz et al. pioneered this area by introducing a QM-based SF (QMScore), a combination of the AM1 SQM method with an empirical dispersion (D) and the PB implicit solvent [eqn (1)]. 17 The method was useful for describing metalloprotein–ligand binding, but further corrections were needed, especially for a quantitative treatment of dispersion and hydrogen bonding. 10 Score = DE int + DDG solv + DG 0w conf À TDS (1) The above equation is a general physics-based SF. The terms are the gas-phase interaction energy (DE int), the change of solvation free energy upon complex formation (DDG solv), the change of conformational 'free' energy (DG 0w conf) and the change of entropy upon ligand binding (ÀTDS). Our approach is systematic. Using accurate calculations in small model systems as a benchmark, we developed corrections for SQM methods that provide reliable and accurate description of a wide range of noncovalent interactions including dispersion , hydrogen-and halogen-bonding. 16 Coupled with the PM6 SQM method, 18 the resulting PM6-D3H4X approach is applicable to a wide chemical space and does not require any