There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Data Mining Practical Machine Learning Tools and Techniques

To be effective as a drug, a potent molecule must reach its target in the body in sufficient concentration, and stay there in a bioactive form long enough for the expected biologic events to occur. Drug development involves assessment of absorption, distribution, metabolism and excretion (ADME) increasingly earlier in the discovery process, at a stage when considered compounds are numerous but access to the physical samples is limited. In that context, computer models constitute valid alternatives to experiments. Here, we present the new SwissADME web tool that gives free access to a pool of fast yet robust predictive models for physicochemical properties, pharmacokinetics, drug-likeness and medicinal chemistry friendliness, among which in-house proficient methods such as the BOILED-Egg, iLOGP and Bioavailability Radar. Easy efficient input and interpretation are ensured thanks to a user-friendly interface through the login-free website http://www.swissadme.ch. Specialists, but also nonexpert in cheminformatics or computational chemistry can predict rapidly key parameters for a collection of molecules to support their drug discovery endeavours.

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SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules

1. Introduction and Overview. 2. Detecting Influential Observations and Outliers. 3. Detecting and Assessing Collinearity. 4. Applications and Remedies. 5. Research Issues and Directions for Extensions. Bibliography. Author Index. Subject Index.

Regression Diagnostics: Identifying Influential Data and Sources of Collinearity

The topology of folded proteins from the representative dataset of well-defined three-dimensional protein structures is studied using a statistical geometry approach. Amino acid residues in protein chains are represented by C alpha atoms, thus reducing the protein three-dimensional structure to a set of points in three dimensional space. The Delaunay tessellation of a protein structure generates an aggregate of space-filling irregular tetrahedra, or Delaunay simplices. Each simplex objectively defines four nearest neighbor C alpha atoms, i.e. four nearest neighbor residues. The statistical analysis of residue composition of Delaunay simplices reveals nonrandom preferences for certain quadruplets of amino acids. These nonrandom preferences are used to develop a fitness function that evaluates sequence-structure compatibility. Using this fitness function, several tested native proteins score the highest among 100,000 random sequences with average protein amino acid composition. The statistical geometry approach, based solely on first principles, provides a unique means for protein structure analysis and has direct implications for inverted protein structure prediction.

Statistical geometry analysis of proteins: implications for inverted structure prediction.

Optimization of chemical library composition affords more efficient identification of hits from biological screening experiments. The optimization could be achieved through rational selection of reagents used in combinatorial library synthesis. However, with a rapid advent of parallel synthesis methods and availability of millions of compounds synthesized by many vendors, it may be more efficient to design targeted libraries by means of virtual screening of commercial compound collections. This chapter reviews the application of advanced cheminformatics approaches such as quantitative structure-activity relationships (QSAR) and pharmacophore modeling (both ligand and structure based) for virtual screening. Both approaches rely on empirical SAR data to build models; thus, the emphasis is placed on achieving models of the highest rigor and external predictive power. We present several examples of successful applications of both approaches for virtual screening to illustrate their utility. We suggest that the expert use of both QSAR and pharmacophore models, either independently or in combination, enables users to achieve targeted libraries enriched with experimentally confirmed hit compounds.

Application of QSAR and shape pharmacophore modeling approaches for targeted chemical library design.

Several series of novel chirality descriptors of chemical organic molecules have been introduced. The descriptors have been developed on the basis of conventional topological descriptors of molecular graphs. They include modified molecular connectivity indices, Zagreb group indices, extended connectivity, overall connectivity, and topological charge indices. These modified descriptors make use of an additional term called chirality correction, which is added to the vertex degrees of asymmetric atoms in a molecular graph. Chirality descriptors can be real or complex numbers. Advantages and drawbacks of different series of chirality descriptors are discussed. These descriptors circumvent the inability of conventional topological indices to distinguish chiral or enantiomeric isomers, which so far has been the major drawback of 2D descriptors as compared to true 3D descriptors (e.g., shape, molecular fields) of molecular structure. These novel chirality descriptors have been implemented in a quantitative stru...

Novel Chirality Descriptors Derived from Molecular Topology.

tion in the availability of resources to support drug discovery and, in particular, databases searchable by molecular structure (Figure 1). Chemistry information on the internet continues to become more widely accessible and at an increasing rate. There are many freely available chemical compound databases on the web1,2. These databases generally contain the chemical identifiers in the form of chemical names (systematic and trade) and registry numbers. Since the files in the databases are assembled in a heterogeneous manner, using variations in deposition processes and procedures to handle chemical structures, the resulting data are plagued with inconsistencies and quality issues. There are many databases available from which the drug discovery community can derive value. These databases generally have a specific focus based on the domain expertise of the hosting organisation; examples include databases of curated literature data, chemical vendor catalogues, patents, analytical data, biological data, etc. There are too many to include in this single article so only a small number will be discussed. For example, the authors recommend a recent article that assesses the expanding public and commercial databases containing bioactive compounds3 and conclude that the commercial efforts are ahead of the public ones. The availability of molecule databases such as PubChem (http://pubchem.ncbi.nlm.nih.gov/) has dramatically changed the landscape of publicly available cheminformatics resources, yet PubChem covers only a fraction of the chemical universe, mostly of interest to chemical genomics and pharmaceutical research. PubChem was By Dr Antony J. Williams, Valery Tkachenko, Dr Chris Lipinski, Professor Alexander Tropsha and Dr Sean Ekins

Free online resources enabling crowd-sourced drug discovery

A proliferation of data sources has led to the notional existence of an implicit Knowledge Graph (KG) that contains vast amounts of biological knowledge contributed by distributed Application Programming Interfaces (APIs). However, challenges arise when integrating data across multiple APIs due to incompatible semantic types, identifier schemes, and data formats. We present ROBOKOP KG (http://robokopkg.renci.org), which is a KG that was initially built to support the open biomedical question-answering application, ROBOKOP (Reasoning Over Biomedical Objects linked in Knowledge-Oriented Pathways) (http://robokop.renci.org). Additionally, we present the ROBOKOP Knowledge Graph Builder (KGB), which constructs the KG and provides an extensible framework to handle graph query over and integration of federated data sources.

Alexander Tropsha

Papers

Statistical geometry analysis of proteins: implications for inverted structure prediction.

Application of QSAR and shape pharmacophore modeling approaches for targeted chemical library design.

Novel Chirality Descriptors Derived from Molecular Topology.

Free online resources enabling crowd-sourced drug discovery

ROBOKOP KG and KGB: Integrated Knowledge Graphs from Federated Sources.