Graph-based Molecular Representation Learning

Molecular representation learning (MRL) is a key step to build the connection between machine learning and chemical science. In particular, it encodes molecules as numerical vectors preserving the molecular structures and features, on top of which the downstream tasks (e.g., property prediction) can be performed. Recently, MRL has achieved considerable progress, especially in methods based on deep molecular graph learning. In this survey, we systematically review these graph-based molecular representation techniques, especially the methods incorporating chemical domain knowledge. Specifically, we first introduce the features of 2D and 3D molecular graphs. Then we summarize and categorize MRL methods into three groups based on their input. Furthermore, we discuss some typical chemical applications supported by MRL. To facilitate studies in this fast-developing area, we also list the benchmarks and commonly used datasets in the paper. Finally, we share our thoughts on future research directions

Identifying Ligand Binding Conformations of the β2-Adrenergic Receptor by Using Its Agonists as Computational Probes

Recently available G-protein coupled receptor (GPCR) structures and biophysical studies suggest that the difference between the effects of various agonists and antagonists cannot be explained by single structures alone, but rather that the conformational ensembles of the proteins need to be considered. Here we use an elastic network model-guided molecular dynamics simulation protocol to generate an ensemble of conformers of a prototypical GPCR, β2-adrenergic receptor (β2AR). The resulting conformers are clustered into groups based on the conformations of the ligand binding site, and distinct conformers from each group are assessed for their binding to known agonists of β2AR. We show that the select ligands bind preferentially to different predicted conformers of β2AR, and identify a role of β2AR extracellular region as an allosteric binding site for larger drugs such as salmeterol. Thus, drugs and ligands can be used as "computational probes" to systematically identify protein conformers with likely biological significance. © 2012 Isin et al

Deciphering Diseases and Biological Targets for Environmental Chemicals using Toxicogenomics Networks

Exposure to environmental chemicals and drugs may have a negative effect on human health. A better understanding of the molecular mechanism of such compounds is needed to determine the risk. We present a high confidence human protein-protein association network built upon the integration of chemical toxicology and systems biology. This computational systems chemical biology model reveals uncharacterized connections between compounds and diseases, thus predicting which compounds may be risk factors for human health. Additionally, the network can be used to identify unexpected potential associations between chemicals and proteins. Examples are shown for chemicals associated with breast cancer, lung cancer and necrosis, and potential protein targets for di-ethylhexyl-phthalate, 2,3,7,8-tetrachlorodibenzo-p-dioxin, pirinixic acid and permethrine. The chemical-protein associations are supported through recent published studies, which illustrate the power of our approach that integrates toxicogenomics data with other data types

A Mapping of Drug Space from the Viewpoint of Small Molecule Metabolism

Small molecule drugs target many core metabolic enzymes in humans and pathogens, often mimicking endogenous ligands. The effects may be therapeutic or toxic, but are frequently unexpected. A large-scale mapping of the intersection between drugs and metabolism is needed to better guide drug discovery. To map the intersection between drugs and metabolism, we have grouped drugs and metabolites by their associated targets and enzymes using ligand-based set signatures created to quantify their degree of similarity in chemical space. The results reveal the chemical space that has been explored for metabolic targets, where successful drugs have been found, and what novel territory remains. To aid other researchers in their drug discovery efforts, we have created an online resource of interactive maps linking drugs to metabolism. These maps predict the “effect space” comprising likely target enzymes for each of the 246 MDDR drug classes in humans. The online resource also provides species-specific interactive drug-metabolism maps for each of the 385 model organisms and pathogens in the BioCyc database collection. Chemical similarity links between drugs and metabolites predict potential toxicity, suggest routes of metabolism, and reveal drug polypharmacology. The metabolic maps enable interactive navigation of the vast biological data on potential metabolic drug targets and the drug chemistry currently available to prosecute those targets. Thus, this work provides a large-scale approach to ligand-based prediction of drug action in small molecule metabolism

Revisiting the Stereodetermining Step in Enantioselective Iridium-Catalyzed Imine Hydrogenation

The mechanism for the iridium-catalyzed asymmetric hydrogenation of prochiral imines has been investigated for an experimentally relevant ligand substrate combination using DFT calculations. The possible stereoisomers of the stereodetermining hydride transfer transition state were considered for four possible hydrogenation mechanisms starting from the recently disclosed active catalyst consisting of iridium phosphine-oxazoline with cyclometalated imine substrate. The hydrogenation was found to proceed via an outer sphere pathway. The transition state accurately describes the experimental observations of the active catalyst and provides a structural rationale for the high stereoinduction despite the lack of direct interaction points in the outer-sphere mechanism. The predicted enantioselectivity was consistent with experimental observations. Experimental studies support the hypothesis that the iridacycle forms spontaneously and functions as the active catalyst in the hydrogenation

Internal and External Stereoisomers of Squaraine Rotaxane Endoperoxide: Synthesis, Chemical Differences, and Structural Revision

Photooxygenation of permanently interlocked squaraine rotaxanes with anthracene-containing macrocycles produces the corresponding squaraine rotaxane endoperoxides (SREPs) quantitatively. SREPs are stored at low temperature, and upon warming, they undergo clean cycloreversion, releasing singlet oxygen and emitting light. The structural elucidation in 2010 assigned the structure as the <b>SREP-int</b> stereoisomer, with the endoperoxide unit directed inside the macrocycle cavity. New experimental and computational evidence reported here proves that the initial, kinetic photooxygenation product is the less stable <b>SREP-ext</b> stereoisomer with the endoperoxide unit directed outside the macrocycle. The photophysical properties and subsequent reactivity of mechanically strained <b>SREP-ext</b> depend on the size of the end groups of the encapsulated squaraine dye. If the end groups are sufficiently large to prevent dissociation of the interlocked components, the strained <b>SREP-ext</b> stereoisomer undergoes clean thermal cycloreversion. However, smaller squaraine end groups allow transient dissociation, resulting in a pseudorotaxane dissociation/association process that produces <b>SREP-int</b> as the thermodynamic stereoisomer that does not cyclorevert. The large difference in endoperoxide reactivity for the two SREP stereoisomers illustrates the power of the mechanical bond to induce cross-component steric strain and selective enhancement of a specific reaction pathway. The new insight enabled synthetic development of triptycene-containing squaraine rotaxanes with high fluorescence quantum yields and large Stokes shifts

Internal and External Stereoisomers of Squaraine Rotaxane Endoperoxide: Synthesis, Chemical Differences, and Structural Revision

Photooxygenation of permanently interlocked squaraine rotaxanes with anthracene-containing macrocycles produces the corresponding squaraine rotaxane endoperoxides (SREPs) quantitatively. SREPs are stored at low temperature, and upon warming, they undergo clean cycloreversion, releasing singlet oxygen and emitting light. The structural elucidation in 2010 assigned the structure as the <b>SREP-int</b> stereoisomer, with the endoperoxide unit directed inside the macrocycle cavity. New experimental and computational evidence reported here proves that the initial, kinetic photooxygenation product is the less stable <b>SREP-ext</b> stereoisomer with the endoperoxide unit directed outside the macrocycle. The photophysical properties and subsequent reactivity of mechanically strained <b>SREP-ext</b> depend on the size of the end groups of the encapsulated squaraine dye. If the end groups are sufficiently large to prevent dissociation of the interlocked components, the strained <b>SREP-ext</b> stereoisomer undergoes clean thermal cycloreversion. However, smaller squaraine end groups allow transient dissociation, resulting in a pseudorotaxane dissociation/association process that produces <b>SREP-int</b> as the thermodynamic stereoisomer that does not cyclorevert. The large difference in endoperoxide reactivity for the two SREP stereoisomers illustrates the power of the mechanical bond to induce cross-component steric strain and selective enhancement of a specific reaction pathway. The new insight enabled synthetic development of triptycene-containing squaraine rotaxanes with high fluorescence quantum yields and large Stokes shifts

Molecular Analysis of Membrane Targeting by the C2 Domain of the E3 Ubiquitin Ligase Smurf1

SMAD ubiquitination regulatory factor 1 (Smurf1) is a Nedd4 family E3 ubiquitin ligase that regulates cell motility, polarity and TGFβ signaling. Smurf1 contains an N-terminal protein kinase C conserved 2 (C2) domain that targets cell membranes and is required for interactions with membrane-localized substrates such as RhoA. Here, we investigated the lipid-binding mechanism of Smurf1 C2, revealing a general affinity for anionic membranes in addition to a selective affinity for phosphoinositides (PIPs). We found that Smurf1 C2 localizes not only to the plasma membrane but also to negatively charged intracellular sites, acting as an anionic charge sensor and selective PIP-binding domain. Site-directed mutagenesis combined with docking/molecular dynamics simulations revealed that the Smurf1 C2 domain loop region primarily interacts with PIPs and cell membranes, as opposed to the β-surface cationic patch employed by other C2 domains. By depleting PIPs from the inner leaflet of the plasma membrane, we found that PIP binding is necessary for plasma membrane localization. Finally, we used a Smurf1 cellular ubiquitination assay to show that the amount of ubiquitin at the plasma membrane interface depends on the lipid-binding properties of Smurf1. This study shows the mechanism by which Smurf1 C2 targets membrane-based substrates and reveals a novel interaction for non-calcium-dependent C2 domains and membrane lipids

On the use of real-world datasets for reaction yield prediction

The lack of publicly available, large, and unbiased datasets is a key bottleneck for the application of machine learning (ML) methods in synthetic chemistry. Data from electronic laboratory notebooks (ELNs) could provide less biased, large datasets, but no such datasets have been made publicly available. The first real-world dataset from the ELNs of a large pharmaceutical company is disclosed and its relationship to high-throughput experimentation (HTE) datasets is described. For chemical yield predictions, a key task in chemical synthesis, an attributed graph neural network (AGNN) performs as well as or better than the best previous models on two HTE datasets for the Suzuki-Miyaura and Buchwald-Hartwig reactions. However, training the AGNN on an ELN dataset does not lead to a predictive model. The implications of using ELN data for training ML-based models are discussed in the context of yield predictions