6.2.2. Definition of Effective Properties 3064 6.3. Response Properties to Magnetic Fields 3066 6.3.1. Nuclear Shielding 3066 6.3.2. Indirect Spin−Spin Coupling 3067 6.3.3. EPR Parameters 3068 6.4. Properties of Chiral Systems 3069 6.4.1. Electronic Circular Dichroism (ECD) 3069 6.4.2. Optical Rotation (OR) 3069 6.4.3. VCD and VROA 3070 7. Continuum and Discrete Models 3071 7.1. Continuum Methods within MD and MC Simulations 3072

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Quantum mechanical continuum solvation models.

Lead- and drug-like compounds: the rule-of-five revolution.

MNDO/AM1‐type parameters for twelve elements have been optimized using a newly developed method for optimizing parameters for semiempirical methods. With the new method, MNDO‐PM3, the average difference between the predicted heats of formation and experimental values for 657 compounds is 7.8 kcal/mol, and for 106 hypervalent compounds, 13.6 kcal/mol. For MNDO the equivalent differences are 13.9 and 75.8 kcal/mol, while those for AM1, in which MNDO parameters are used for aluminum, phosphorus, and sulfur, are 12.7 and 83.1 kcal/mol, respectively. Average errors for ionization potentials, bond angles, and dipole moments are intermediate between those for MNDO and AM1, while errors in bond lengths are slightly reduced.

Optimization of parameters for semiempirical methods II. Applications

PubChem (https://pubchem.ncbi.nlm.nih.gov) is a public repository for information on chemical substances and their biological activities, launched in 2004 as a component of the Molecular Libraries Roadmap Initiatives of the US National Institutes of Health (NIH). For the past 11 years, PubChem has grown to a sizable system, serving as a chemical information resource for the scientific research community. PubChem consists of three inter-linked databases, Substance, Compound and BioAssay. The Substance database contains chemical information deposited by individual data contributors to PubChem, and the Compound database stores unique chemical structures extracted from the Substance database. Biological activity data of chemical substances tested in assay experiments are contained in the BioAssay database. This paper provides an overview of the PubChem Substance and Compound databases, including data sources and contents, data organization, data submission using PubChem Upload, chemical structure standardization, web-based interfaces for textual and non-textual searches, and programmatic access. It also gives a brief description of PubChem3D, a resource derived from theoretical three-dimensional structures of compounds in PubChem, as well as PubChemRDF, Resource Description Framework (RDF)-formatted PubChem data for data sharing, analysis and integration with information contained in other databases.

PubChem Substance and Compound databases

Computational approaches that 'dock' small molecules into the structures of macromolecular targets and 'score' their potential complementarity to binding sites are widely used in hit identification and lead optimization Indeed, there are now a number of drugs whose development was heavily influenced by or based on structure-based design and screening strategies, such as HIV protease inhibitors Nevertheless, there remain significant challenges in the application of these approaches, in particular in relation to current scoring schemes Here, we review key concepts and specific features of small-molecule-protein docking methods, highlight selected applications and discuss recent advances that aim to address the acknowledged limitations of established approaches

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Docking and scoring in virtual screening for drug discovery: methods and applications.

The judicious application of ligand or binding efficiency metrics, which quantify the molecular properties required to obtain binding affinity for a drug target, is gaining traction in the selection and optimization of fragments, hits and leads. Retrospective analysis of recently marketed oral drugs shows that they frequently have highly optimized ligand efficiency values for their targets. Optimizing ligand efficiency metrics based on both molecular mass and lipophilicity, when set in the context of the specific target, has the potential to ameliorate the inflation of these properties that has been observed in current medicinal chemistry practice, and to increase the quality of drug candidates.

/pdf/the-role-of-ligand-efficiency-metrics-in-drug-discovery-3w96f9rlcx.pdf

The role of ligand efficiency metrics in drug discovery

Ligand efficiency (i.e., potency/size) has emerged as an important metric in drug discovery. In general, smaller, more efficient ligands are believed to have improved prospects for good drug properties (e.g., bioavailability). Our analysis of thousands of ligands across a variety of targets shows that ligand efficiency is dependent on ligand size with smaller ligands having greater efficiencies, on average, than larger ligands. We propose two primary causes for this size dependence: the inevitable reduction in the quality of fit between ligand and receptor as the ligand becomes larger and more complex and the reduction in accessible ligand surface area on a per atom basis as size increases. These results have far-ranging implications for analysis of high-throughput screening hits, fragment-based approaches to drug discovery, and even computational models of potency.

Ligand binding efficiency: trends, physical basis, and implications.

The MNDO parameters for sulfur have been reoptimized. Calculations for a number of sulfur compounds indicate a very significant improvement. Inclusion of d AOs failed to correct the errors for compounds of sulfur in its higher valence states. Since d AOs are not included, the calculations are still confined to compounds of divalent sulfur.

An improved set of mndo parameters for sulfur

2D fragment-based similarity searching is one of the most popular techniques for searching a large database of chemical structures and has been widely applied in drug discovery. However, its performance, especially its effectiveness in retrieving active structural analogues, has not been adequately studied. We report a series of computational experiments, where we systematically studied the influence of structural descriptors and similarity coefficients on the effectiveness of similarity searching. The study was conducted using two public large data sets, NCI anti-AIDS and MDDR. Four sets of 2D linear fragment descriptors, based on the original definitions of atom pairs and atom sequences, were compared. The effect of using the Tanimoto coefficient and the Euclidean distance was studied as a function of descriptor set. The results clearly indicate that the Tanimoto coefficient is superior to the Euclidean distance in 2D-fragment based similarity searching, in terms of hit rate, while atom sequences demonstrate the best overall performance among the structural descriptors we studied.

Charles H. Reynolds

Papers

The role of ligand efficiency metrics in drug discovery

Ligand binding efficiency: trends, physical basis, and implications.

An improved set of mndo parameters for sulfur

Performance of similarity measures in 2D fragment-based similarity searching: comparison of structural descriptors and similarity coefficients.

Ligand efficiency and fragment-based drug discovery.