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.

/pdf/swissadme-a-free-web-tool-to-evaluate-pharmacokinetics-drug-xebymauqho.pdf

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

In the wake of recent COVID-19 pandemics scientists around the world rushed to deliver numerous CADD (Computer-Aided Drug Discovery) methods and tools that could be reliably used to discover novel drug candidates against the SARS-CoV-2 virus. With that, there emerged a trend of a significant democratization of CADD that contributed to the rapid development of various COVID-19 drug candidates currently undergoing different stages of validation. On the other hand, this democratization also inadvertently led to the surge rapidly performed molecular docking studies to nominate multiple scores of novel drug candidates supported by computational arguments only. Albeit driven by best intentions, most of such studies also did not follow best practices in the field that require experience and expertise learned through multiple rigorously designed benchmarking studies and rigorous experimental validation. In this Viewpoint we reflect on recent disbalance between small number of rigorous and comprehensive studies and the proliferation of purely computational studies enabled by the ease of docking software availability. We further elaborate on the hyped oversale of CADD methods' ability to rapidly yield viable drug candidates and reiterate the critical importance of rigor and adherence to the best practices of CADD in view of recent emergence of AI and Big Data in the field.

Surely you are joking, Mr Docking!

Energetics of enzyme stability: Response from Carter, Tropsha and Edgell

Many laboratories working in the field of drug discovery use the ZINC database to identify and then acquire commercially available chemicals. However, finding the best deal for a given compound is often time-intensive and laborious, as the process involves searching for all vendors selling the desired compound, comparing prices, and interacting with the preferred vendor. To streamline this process, we have developed ZINC Express, a web application that simplifies the online purchase of chemicals annotated in the ZINC database. For any compound with a known ZINC ID, ZINC Express finds a list of vendors offering that compound and for each such vendor returns the available package quantities, the price of each package, and the price per milligram along with a link to that vendor. We expect that ZINC Express will be of use to both computational and experimental researchers. ZINC Express is freely accessible online at https://zincexpress.mml.unc.edu/.

ZINC Express: A Virtual Assistant for Purchasing Compounds Annotated in the ZINC Database.

Knowledge-graph (KG) embeddings have emerged as a promise in addressing challenges faced by modern biomedical research, including the growing gap between therapeutic needs and available treatments. The popularity of KG embeddings in graph analytics is on the rise, due at least partially to the presumed semanticity of the learned embeddings. Unfortunately, the ability of a node neighborhood picked up by an embedding to capture the node's semantics may depend on the characteristics of the data. One of the reasons for this problem is that KG nodes can be promiscuous, that is, associated with a number of different relationships that are not unique or indicative of the properties of the nodes. To address the promiscuity challenge and the documented runtime-performance challenge in real-life KG embedding tools, we propose to use domain- and task-specific information to specify regular-expression pathways that define neighborhoods of KG nodes of interest. Our proposed CompactWalks framework uses these semantic subgraphs to enable meaningful compact walks in random-walk based KG embedding methods. We report the results of case studies for the task of determining which pharmaceutical drugs could treat the same diseases. The findings suggest that our CompactWalks approach has the potential to address the promiscuity and runtime-performance challenges in applying embedding tools to large-scale KGs in real life, in the biomedical domain and possibly beyond.

Compact Walks: Taming Knowledge-Graph Embeddings with Domain- and Task-Specific Pathways

Part 1. We develop a simple pre-conditioning method applicable to any integer program with integral data. It replaces the feasibility problem {Ax ≤ b, x ∈ Zn } by the equivalent problem {(AU)y ≤ b, y ∈ Zn } where U is a unimodular matrix such that the columns of AU are “relatively” short and orthogonal. In practice, Ug is found by Lovasz basis reduction or by other related algorithms. An analysis of this method for several classes of hard knapsack problems is presented. We prove that branch-and-bound must take an exponential number of nodes to solve these problems, while after reformulation, it solves them by enumerating a constant number of nodes. In one prominent instance, a 0-1 knapsack problem with only 100 variables that could not be solved by the commercial solver CPLEX after enumerating 2 billion nodes, was solved at the root node after the reformulation was applied. This work was done under the guidance of Gabor Pataki. 
Part 2. The first problem deals with the development of a four-body statistical scoring function for discriminating correct from incorrect protein structures. We represent proteins by the side chain centroid coordinates of their amino acids. The Delaunay tessellation of this representation defines all sets of nearest neighbor quadruplets of amino acids. Four-body contact scoring function (log likelihoods of residue quadruplet compositions) is derived by the analysis of a diverse set of proteins with known structures. A test protein is characterized by the total score calculated as the sum of the individual log likelihoods of composing amino acid quadruplets. The four-body scoring function distinguishes native from partially unfolded or deliberately mis-folded structures for several classes of decoy sets. It also discriminates between pre- and post-transition state structures in the folding simulations trajectory of Chymotrypsin Inhibitor 2 (CI2) and SH3. We also propose that individual groups making predictions for a target protein could use our scoring function to accurately rank their candidate models. Testing this hypothesis on the 3D coordinate models submitted for the targets from CASP5, we conclude that, on an average, individual groups would have made more accurate rankings of their predictions had they used the four-body scoring function for this purpose. (Abstract shortened by UMI.)

Alexander Tropsha

Papers

Surely you are joking, Mr Docking!

Energetics of enzyme stability: Response from Carter, Tropsha and Edgell

ZINC Express: A Virtual Assistant for Purchasing Compounds Annotated in the ZINC Database.

Compact Walks: Taming Knowledge-Graph Embeddings with Domain- and Task-Specific Pathways

Pre-conditioning integer programs using column basis reduction and geometry and topology of protein structure