Lead Drug Screening, Scoring, and Ranking

Lead compounds show pharmacological activity against a biological target and the progressive optimization of the pharmacological properties. Measurements such as potency, selectivity, solubility, permeability, metabolic stability, low Cytochrome P450 (CYP) inhibition and good pharmacokinetic (PK) properties tend to apply across most small-molecule discovery programs, as well as the increasing use of calculations to confine lipophilicity (logP/ cLogP), molecular weight and a growing number of other molecular descriptors. Various new guideposts such as ligand efficiency (LE), lipophilic efficiency (LipE), and lipophilic ligand efficiency (LLE), rotatable bonds and topological polar surface area (TPSA) have also been introduced. Further assays and computational approaches that attempted to model absorption, distribution, metabolism, excretion and toxicity (ADMET) are developed. Molecular docking algorithms are regularly used to virtual screen large numbers of compounds to identify potential leads. In this mode, parameters of the docking algorithm are often modified to optimize the speed of calculation.

Figure 1 Virtual Screening- improved hit to lead screening process

Screening

Figure 2 Workflow of Screening

Database screening process, and large libraries of drug-like compounds for screening.
Based on the primary biological activity, and the fate of compounds in later in vivo assays.
Caco-2 evaluation (faster than 22 nm/s) and PAMPA assay data used as filter.
Eliminate spurious hits and artifacts in orthogonal assay.
Homology modeling, molecular Dynamics (MD) simulations, SAR analysis.
Experimental assessment of undesirable off-target activities.
ADMET assays and proper selectivity assays to identify weaknesses within a series of compounds.
Available softwares include PROPKA, H++, SPORES, PDB2PQR, 3D RISM, SZMAP, JAWS, WaterMap, etc.
Further filtering by MW<300, logP<3, number of hydrogen bond donors and acceptors<3, number of rotatable bonds <3, 40<molar refractivity<130, 20<number of atoms<70, polar surface area no greater than 140 Å2, etc.
Regularly evaluates screening funnel to ensure the data are required.
Develop new intellectual property in order to protect the drug candidate by patents.

Docking and Scoring

Lead scoring helps you make the most of all your resources, generating:

More successful compounds campaigns
More & higher quality leads
More effective lead nurturing
Higher conversion rates & revenue

A scoring function is utilized in docking to approximate the free energy of binding between the protein and the ligand in each docking pose. Other scoring functions, like force field-based functions, empirical scoring functions, knowledge-based functions, have hitherto been developed. The scores for each compound are added across all the scoring parameters to give a final score, which can be used for ranking prospects. Use X-ray, NMR or neutron scattering spectroscopy to reveal much of the conformational information of the flexible molecules. Docking models are derived from crystal structures of the target or homologous proteins, with the help of computational methods and the 3D structural information. Docking programs include Autodock Tools, LigPrep, MOE, MAPS platform, DISI, Pipeline pilot, Hyperchem, etc. Applying various virtual screening protocols (docking, 2D molecular similarity, and 3D molecular similarity) resulted in achieving better lead rates.

Figure 3 Conformational search methods utilized in docking programs

Ranking

Docking is followed by scoring in chemical drug design in order to rank the compounds. The categories used to help rank leads include calculated parameters, experimentally-derived primary assay data, secondary assay data and kinetic solubility. Visual inspection of thousands of docking poses is normally needed by the medicinal chemist in order to select the appropriate compound set for assaying.