Epstein-Barr virus.

Drug-likeness is a key consideration when selecting compounds during the early stages of drug discovery. However, evaluation of drug-likeness in absolute terms does not reflect adequately the whole spectrum of compound quality. More worryingly, widely used rules may inadvertently foster undesirable molecular property inflation as they permit the encroachment of rule-compliant compounds towards their boundaries. We propose a measure of drug-likeness based on the concept of desirability called the quantitative estimate of drug-likeness (QED). The empirical rationale of QED reflects the underlying distribution of molecular properties. QED is intuitive, transparent, straightforward to implement in many practical settings and allows compounds to be ranked by their relative merit. We extended the utility of QED by applying it to the problem of molecular target druggability assessment by prioritizing a large set of published bioactive compounds. The measure may also capture the abstract notion of aesthetics in medicinal chemistry.

/pdf/quantifying-the-chemical-beauty-of-drugs-48hax4bgv8.pdf

Quantifying the chemical beauty of drugs

Pharmaceutical research has successfully incorporated a wealth of molecular modeling methods, within a variety of drug discovery programs, to study complex biological and chemical systems. The integration of computational and experimental strategies has been of great value in the identification and development of novel promising compounds. Broadly used in modern drug design, molecular docking methods explore the ligand conformations adopted within the binding sites of macromolecular targets. This approach also estimates the ligand-receptor binding free energy by evaluating critical phenomena involved in the intermolecular recognition process. Today, as a variety of docking algorithms are available, an understanding of the advantages and limitations of each method is of fundamental importance in the development of effective strategies and the generation of relevant results. The purpose of this review is to examine current molecular docking strategies used in drug discovery and medicinal chemistry, exploring the advances in the field and the role played by the integration of structure- and ligand-based methods.

Molecular Docking and Structure-Based Drug Design Strategies

The differences between three different compound classes, natural products, molecules from combinatorial synthesis, and drug molecules, were investigated. The major structural differences between natural and combinatorial compounds originate mainly from properties introduced to make combinatorial synthesis more efficient. These include the number of chiral centers, the prevalence of aromatic rings, the introduction of complex ring systems, and the degree of the saturation of the molecule as well as the number and ratios of different heteroatoms. As drug molecules derive from both natural and synthetic sources, they cover a joint area in property space of natural and combinatorial compounds. A PCA-based scheme is presented that differentiates the three classes of compounds. It is suggested that by mimicking certain distribution properties of natural compounds, combinatorial products might be made that are substantially more diverse and have greater biological relevance.

Property distributions: differences between drugs, natural products, and molecules from combinatorial chemistry.

Ten measures of experimental electron-density-map quality are examined and the skewness of electron density is found to be the best indicator of actual map quality. A Bayesian approach to estimating map quality is developed and used in the PHENIX AutoSol wizard to make decisions during automated structure solution.

Decision-making in structure solution using Bayesian estimates of map quality: the PHENIX autosol wizard

This article reviews current achievements in the field of chemoinformatics and their impact on modern drug discovery processes. The main data mining approaches used in cheminformatics, such as descriptor computations, structural similarity matrices, and classification algorithms, are outlined. The applications of cheminformatics in drug discovery, such as compound selection, virtual library generation, virtual high throughput screening, HTS data mining, and in silico ADMET are discussed. At the conclusion, future directions of chemoinformatics are suggested.

https://www.mdpi.com/1420-3049/7/8/566/pdf

Chemoinformatics and Drug Discovery

Synthesis planning is the process of recursively decomposing target molecules into available precursors. Computer-aided retrosynthesis can potentially assist chemists in designing synthetic routes; however, at present, it is cumbersome and cannot provide satisfactory results. In this study, we have developed a template-free self-corrected retrosynthesis predictor (SCROP) to predict retrosynthesis using transformer neural networks. In the method, the retrosynthesis planning was converted to a machine translation problem from the products to molecular linear notations of the reactants. By coupling with a neural network-based syntax corrector, our method achieved an accuracy of 59.0% on a standard benchmark data set, which outperformed other deep learning methods by >21% and template-based methods by >6%. More importantly, our method was 1.7 times more accurate than other state-of-the-art methods for compounds not appearing in the training set.

Predicting Retrosynthetic Reactions Using Self-Corrected Transformer Neural Networks.

Combinatorial organic synthesis (combinatorial chemistry or CC) and ultrahigh-throughput screening (UHTS) are speeding up drug discovery by increasing capacity for making and screening large numbers of compounds. However, a key problem is to select the smaller set of "representative" compounds from a virtual library to make or screen. Our approach is to select drug-like as well as structurally diverse compounds. The compounds, which are not very drug-like, are less taken into account or excluded even if they contribute to the diversity of the collection. Hence, the first step in the compound selection is to rank compounds in drug-like "degree". To quantify the drug-like "degree", drug-like index (DLI) is introduced in this paper. A compound's DLI is calculated based upon the knowledge derived from known drugs selected from Comprehensive Medicinal Chemistry (CMC) database. The paper describes the way of this knowledge base is formed and the procedure for selecting drug-like compounds.

Drug-like Index: A New Approach To Measure Drug-like Compounds and Their Diversity

From monoclonal antibodies to small molecules: the development of inhibitors targeting the PD-1/PD-L1 pathway.

Identifying novel drug–protein interactions is crucial for drug
discovery. For this purpose, many machine learning-based methods have been developed
based on drug descriptors and one-dimensional protein sequences. However, protein
sequences cannot accurately reflect the interactions in three-dimensional space.
However, direct input of three-dimensional structure is of low efficiency due to the
sparse three-dimensional matrix, and is also prevented by the limited number of
co-crystal structures available for training. Here we propose an end-to-end deep
learning framework to predict the interactions by representing proteins with a
two-dimensional distance map from monomer structures (Image) and drugs with
molecular linear notation (String), following the visual question answering mode.
For efficient training of the system, we introduce a dynamic attentive convolutional
neural network to learn fixed-size representations from the variable-length distance
maps and a self-attentional sequential model to automatically extract semantic
features from the linear notations. Extensive experiments demonstrate that our model
obtains competitive performance against state-of-the-art baselines on the directory
of useful decoys, enhanced (DUD-E), human and BindingDB benchmark datasets. Further
attention visualization provides biological interpretation to depict highlighted
regions of both protein and drug molecules. When predicting the interaction of proteins with potential drugs, the
protein can be encoded as its one-dimensional sequence or a three-dimensional
structure, which can capture more relevant features of the protein, but also makes
the task to predict the interactions harder. A new method predicts these
interactions using a two-dimensional distance matrix representation of a protein,
which can be processed like a two-dimensional image, striking a balance between the
data being simple to process and rich in relevant structures.

/pdf/predicting-drug-protein-interaction-using-quasi-visual-1btt4llr6i.pdf

Jun Xu

Papers

Chemoinformatics and Drug Discovery

Predicting Retrosynthetic Reactions Using Self-Corrected Transformer Neural Networks.

Drug-like Index: A New Approach To Measure Drug-like Compounds and Their Diversity

From monoclonal antibodies to small molecules: the development of inhibitors targeting the PD-1/PD-L1 pathway.

Predicting drug–protein interaction using quasi-visual question answering system