A Simple Representation of Three-Dimensional Molecular Structure

Axen, Seth D.; Huang, Xi Ping; Cáceres, Elena L.; Gendelev, Leo; Roth, Bryan L.; Keiser, Michael J.

doi:10.1021/acs.jmedchem.7b00696

Cited by 88 publications

(88 citation statements)

References 92 publications

(206 reference statements)

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“…[41,102,203] A second challenge is the representation of molecular structures (or more general of any material) in a machine learning approach. [206] Current achievements in reaction prediction and retrosynthesis demonstrate the potential of machine learning to solve one of the bottlenecks of state-of-the-art materials design, which is the planning of efficient reaction routes of new molecules. [206] Current achievements in reaction prediction and retrosynthesis demonstrate the potential of machine learning to solve one of the bottlenecks of state-of-the-art materials design, which is the planning of efficient reaction routes of new molecules.…”

Section: Discussionmentioning

confidence: 99%

Toward Design of Novel Materials for Organic Electronics

et al. 2019

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organic/inorganic perovskites solar cells [6] to printable electronic circuits based on organic field-effect transistors (OFETs). [7] While OLED displays outperform their inorganic counterparts in terms of energy efficiency, [8] scientific and technical challenges concerning the stability and processability of the organic materials used in large-area OLEDs and organic solar cells remain. New challenges arise from applications, such as displays on flexible substrates, OLED lightning, large area displays as well as for printable or solution processable larger area solar cells. [8] Many of the remaining challenges are material related, e.g., the low mobility of charge carriers in organic materials in general and in amorphous organic semiconductors in particular. There are other materials related issues, such as limited OLED life times due to unstable blue hosts and emitters, [9] low fill factors, and therefore reduced power conversion efficiencies of organic solar cells, [10,11] low conductivity and high costs of organic charge transport layers of perovskite solar cells [6,12,13] and low conductivity and hard processability of crystalline OFET materials. [14] Conductivity and injection can be improved by doping the organic thin films with molecular dopants with high electron affinities (p-type) [15] or low ionization energies (n-type). [16] The doping mechanism of organic materials is in many cases not well understood, [17] making material and device optimization a costly experimental endeavor.The development of better materials is presently based on chemical insight, in part guided by theoretical understanding, or the experimental screening of large numbers of compounds. Given the size of the potentially available chemical space this remains a costly and time-consuming approach. Recent successes in experimental design of novel materials and concepts include the development of a stable strong molecular n-type dopant, [16] a study about the quantitative relation between interaction parameter, miscibility, and function of conjugated polymer donors and small-molecule acceptors for bulk heterojunctions as used in organic solar cells [18] the development of a universal strategy for ohmic hole injection into organic semiconductors with high ionization energies [19] and many others. [20][21][22][23][24][25] Another example is the development of strategies to harvest triplet excitons in organic light emitting diodes. For this purpose, novel classes of emitter molecules were developed, which include thermally activated delayed fluorescence (TADF)-based molecules, [26][27][28][29][30][31][32] rotationally accessed spin-state inversion, [33] and radical-based emitters. [34] Materials for organic electronics are presently used in prominent applications, such as displays in mobile devices, while being intensely researched for other purposes, such as organic photovoltaics, large-area devices, and thin-film transistors. Many of the challenges to improve and optimize these applications are material related and there is a nearly inf...

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Section: Discussionmentioning

confidence: 99%

Toward Design of Novel Materials for Organic Electronics

et al. 2019

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“…[34,37,38] Shape-based molecular descriptors can also be represented as fingerprints (e.g.,e xtended three-dimensional fingerprints (E3FP)). [39] Descriptors based on the concept of the pharmacophore (i.e.,t he pattern of features of am olecule responsible for its biological effect [40] )a ssess molecular similarity,i nt erms of the presenceo ra bsence of pharmacophoric features, such as positive/negativec harges,H Don/HAcc, and aromatic or hydrophobic moieties. Typically,t heir topological or spatiala rrangement is also taken into account; [34,35,41,42] hence, most pharmacophore-based descriptors are hybrids.…”

Section: Diversity Of Chemical Librariesmentioning

confidence: 99%

The Symbiotic Relationship Between Drug Discovery and Organic Chemistry

Grygorenko

Volochnyuk

Ryabukhin

et al. 2019

Chemistry A European J

124

123

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All pharmaceutical products contain organic molecules; the source may be a natural product or a fully synthetic molecule, or a combination of both. Thus, it follows that organic chemistry underpins both existing and upcoming pharmaceutical products. The reverse relationship has also affected organic synthesis, changing its landscape towards increasingly complex targets. This Review article sets out to give a concise appraisal of this symbiotic relationship between organic chemistry and drug discovery, along with a discussion of the design concepts and highlighting key milestones along the journey. In particular, criteria for a high‐quality compound library design enabling efficient virtual navigation of chemical space, as well as rise and fall of concepts for its synthetic exploration (such as combinatorial chemistry; diversity‐, biology‐, lead‐, or fragment‐oriented syntheses; and DNA‐encoded libraries) are critically surveyed.

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“…A2: 3D fingerprints. 1024-bit E3FP fingerprints (https://github.com/keiserlab/e3fp) were calculated by merging the results of the three best conformers obtained with a UFF energy minimization, as recommended in the E3FP publication 42 .…”

Section: A: Chemistrymentioning

confidence: 99%

Extending the small molecule similarity principle to all levels of biology

Duran‐Frigola

Pauls

Guitart-Pla

et al. 2019

Preprint

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We present the Chemical Checker (CC), a resource that provides processed, harmonized and integrated bioactivity data on 800,000 small molecules. The CC divides data into five levels of increasing complexity, ranging from the chemical properties of compounds to their clinical outcomes. In between, it considers targets, off-targets, perturbed biological networks and several cell-based assays such as gene expression, growth inhibition and morphological profilings. In the CC, bioactivity data are expressed in a vector format, which naturally extends the notion of chemical similarity between compounds to similarities between bioactivity signatures of different kinds. We show how CC signatures can boost the performance of drug discovery tasks that typically capitalize on chemical descriptors, including target identification and library characterization. Moreover, we demonstrate and experimentally validate that CC signatures can be used to reverse and mimic biological signatures of disease models and genetic perturbations, options that are otherwise impossible using chemical information alone. an organic molecule (A: Chemistry) that interacts with one or several protein receptors (B: Targets), triggering perturbations of biological pathways (C: Networks) and eliciting phenotypic outcomes that can be measured in e.g. cell-based assays (D: Cells) before delivery to patients (E: Clinics). Using these five categories, we classified the information stored in major compound databases, including chemogenomics resources, cell-based screens and, when available, clinical reports of drug effects (Methods).We then divided each level (A-E) into five sublevels (1-5) corresponding to distinct types or scopes of the data. In total, the CC contains 25 well-defined categories meant to illustrate the most relevant aspects of small molecule characterization. In particular, we stored the 2D (A1) and 3D (A2) structures of compounds, together with their scaffolds (A3), functional groups (A4) and physicochemistry (A5). We also retrieved therapeutic targets (B1) and drug metabolizing enzymes (B2), and molecules co-crystallized with protein chains (B3). We fetched literature binding data (B4) from major chemogenomics databases, and high-throughput target screening results (B5). Moving to a higher order of biology, we looked for ontological classifications of compounds (C1) and focused on human metabolites in a genomescale metabolic network (C2). In addition, we kept the pathways (C3), biological processes (C4) and protein-protein interactions (C5) of the previously collected binding data. To capture cell-level information, we gathered differential gene expression profiles (D1) and compound growth-inhibition potencies across cancer cell lines (D2). Similarly, we gathered sensitivity profiles over an array of yeast mutants (chemical genetics) (D3), as well as cell morphology changes (high-content screening) (D4). Additional cell sensitivity data available from the literature were also collected (D5). To organize clinical data, we used the traditio...

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A Simple Representation of Three-Dimensional Molecular Structure

Cited by 88 publications

References 92 publications

Toward Design of Novel Materials for Organic Electronics

Toward Design of Novel Materials for Organic Electronics

The Symbiotic Relationship Between Drug Discovery and Organic Chemistry

Extending the small molecule similarity principle to all levels of biology

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