Better Informed Distance Geometry: Using What We Know To Improve Conformation Generation

Small organic molecules are often flexible, i.e., they can adopt a variety of low-energy conformations in solution that exist in equilibrium with each other. Two main search strategies are used to generate representative conformational ensembles for molecules: systematic and stochastic. In the first approach, each rotatable bond is sampled systematically in discrete intervals, limiting its use to molecules with a small number of rotatable bonds. Stochastic methods, on the other hand, sample the conformational space of a molecule randomly and can thus be applied to more flexible molecules. Different methods employ different degrees of experimental data for conformer generation. So-called knowledge-based methods use predefined libraries of torsional angles and ring conformations. In the distance geometry approach, on the other hand, a smaller amount of empirical information is used, i.e., ideal bond lengths, ideal bond angles, and a few ideal torsional angles. Distance geometry is a computationally fast method to generate conformers, but it has the downside that purely distance-based constraints tend to lead to distorted aromatic rings and sp² centers. To correct this, the resulting conformations are often minimized with a force field, adding computational complexity and run time. Here we present an alternative strategy that combines the distance geometry approach with experimental torsion-angle preferences obtained from small-molecule crystallographic data. The torsional angles are described by a previously developed set of hierarchically structured SMARTS patterns. The new approach is implemented in the open-source cheminformatics library RDKit, and its performance is assessed by comparing the diversity of the generated ensemble and the ability to reproduce crystal conformations taken from the crystal structures of small molecules and protein–ligand complexes

Combining IC₅₀ or <i>K</i>_<i>i</i> Values from Different Sources Is a Source of Significant Noise

As part of the ongoing quest to find or construct large data sets for use in validating new machine learning (ML) approaches for bioactivity prediction, it has become distressingly common for researchers to combine literature IC50 data generated using different assays into a single data set. It is well-known that there are many situations where this is a scientifically risky thing to do, even when the assays are against exactly the same target, but the risks of assays being incompatible are even higher when pulling data from large collections of literature data like ChEMBL. Here, we estimate the amount of noise present in combined data sets using cases where measurements for the same compound are reported in multiple assays against the same target. This approach shows that IC50 assays selected using minimal curation settings have poor agreement with each other: almost 65% of the points differ by more than 0.3 log units, 27% differ by more than one log unit, and the correlation between the assays, as measured by Kendall’s τ, is only 0.51. Requiring that most of the assay metadata in ChEMBL matches (“maximal curation”) in order to combine two assays improves the situation (48% of the points differ by more than 0.3 log units, 13% by more than one log unit, and Kendall’s τ is 0.71) at the expense of having smaller data sets. Surprisingly, our analysis shows similar amounts of noise when combining data from different literature Ki assays. We suggest that good scientific practice requires careful curation when combining data sets from different assays and hope that our maximal curation strategy will help to improve the quality of the data that are being used to build and validate ML models for bioactivity prediction. To help achieve this, the code and ChEMBL queries that we used for the maximal curation approach are available as open-source software in our GitHub repository, https://github.com/rinikerlab/overlapping_assays

Better Informed Distance Geometry: Using What We Know To Improve Conformation Generation

Small organic molecules are often flexible, i.e., they can adopt a variety of low-energy conformations in solution that exist in equilibrium with each other. Two main search strategies are used to generate representative conformational ensembles for molecules: systematic and stochastic. In the first approach, each rotatable bond is sampled systematically in discrete intervals, limiting its use to molecules with a small number of rotatable bonds. Stochastic methods, on the other hand, sample the conformational space of a molecule randomly and can thus be applied to more flexible molecules. Different methods employ different degrees of experimental data for conformer generation. So-called knowledge-based methods use predefined libraries of torsional angles and ring conformations. In the distance geometry approach, on the other hand, a smaller amount of empirical information is used, i.e., ideal bond lengths, ideal bond angles, and a few ideal torsional angles. Distance geometry is a computationally fast method to generate conformers, but it has the downside that purely distance-based constraints tend to lead to distorted aromatic rings and sp² centers. To correct this, the resulting conformations are often minimized with a force field, adding computational complexity and run time. Here we present an alternative strategy that combines the distance geometry approach with experimental torsion-angle preferences obtained from small-molecule crystallographic data. The torsional angles are described by a previously developed set of hierarchically structured SMARTS patterns. The new approach is implemented in the open-source cheminformatics library RDKit, and its performance is assessed by comparing the diversity of the generated ensemble and the ability to reproduce crystal conformations taken from the crystal structures of small molecules and protein–ligand complexes

Better Informed Distance Geometry: Using What We Know To Improve Conformation Generation

Small organic molecules are often flexible, i.e., they can adopt a variety of low-energy conformations in solution that exist in equilibrium with each other. Two main search strategies are used to generate representative conformational ensembles for molecules: systematic and stochastic. In the first approach, each rotatable bond is sampled systematically in discrete intervals, limiting its use to molecules with a small number of rotatable bonds. Stochastic methods, on the other hand, sample the conformational space of a molecule randomly and can thus be applied to more flexible molecules. Different methods employ different degrees of experimental data for conformer generation. So-called knowledge-based methods use predefined libraries of torsional angles and ring conformations. In the distance geometry approach, on the other hand, a smaller amount of empirical information is used, i.e., ideal bond lengths, ideal bond angles, and a few ideal torsional angles. Distance geometry is a computationally fast method to generate conformers, but it has the downside that purely distance-based constraints tend to lead to distorted aromatic rings and sp² centers. To correct this, the resulting conformations are often minimized with a force field, adding computational complexity and run time. Here we present an alternative strategy that combines the distance geometry approach with experimental torsion-angle preferences obtained from small-molecule crystallographic data. The torsional angles are described by a previously developed set of hierarchically structured SMARTS patterns. The new approach is implemented in the open-source cheminformatics library RDKit, and its performance is assessed by comparing the diversity of the generated ensemble and the ability to reproduce crystal conformations taken from the crystal structures of small molecules and protein–ligand complexes

Better Informed Distance Geometry: Using What We Know To Improve Conformation Generation

Small organic molecules are often flexible, i.e., they can adopt a variety of low-energy conformations in solution that exist in equilibrium with each other. Two main search strategies are used to generate representative conformational ensembles for molecules: systematic and stochastic. In the first approach, each rotatable bond is sampled systematically in discrete intervals, limiting its use to molecules with a small number of rotatable bonds. Stochastic methods, on the other hand, sample the conformational space of a molecule randomly and can thus be applied to more flexible molecules. Different methods employ different degrees of experimental data for conformer generation. So-called knowledge-based methods use predefined libraries of torsional angles and ring conformations. In the distance geometry approach, on the other hand, a smaller amount of empirical information is used, i.e., ideal bond lengths, ideal bond angles, and a few ideal torsional angles. Distance geometry is a computationally fast method to generate conformers, but it has the downside that purely distance-based constraints tend to lead to distorted aromatic rings and sp² centers. To correct this, the resulting conformations are often minimized with a force field, adding computational complexity and run time. Here we present an alternative strategy that combines the distance geometry approach with experimental torsion-angle preferences obtained from small-molecule crystallographic data. The torsional angles are described by a previously developed set of hierarchically structured SMARTS patterns. The new approach is implemented in the open-source cheminformatics library RDKit, and its performance is assessed by comparing the diversity of the generated ensemble and the ability to reproduce crystal conformations taken from the crystal structures of small molecules and protein–ligand complexes

Better Informed Distance Geometry: Using What We Know To Improve Conformation Generation

Small organic molecules are often flexible, i.e., they can adopt a variety of low-energy conformations in solution that exist in equilibrium with each other. Two main search strategies are used to generate representative conformational ensembles for molecules: systematic and stochastic. In the first approach, each rotatable bond is sampled systematically in discrete intervals, limiting its use to molecules with a small number of rotatable bonds. Stochastic methods, on the other hand, sample the conformational space of a molecule randomly and can thus be applied to more flexible molecules. Different methods employ different degrees of experimental data for conformer generation. So-called knowledge-based methods use predefined libraries of torsional angles and ring conformations. In the distance geometry approach, on the other hand, a smaller amount of empirical information is used, i.e., ideal bond lengths, ideal bond angles, and a few ideal torsional angles. Distance geometry is a computationally fast method to generate conformers, but it has the downside that purely distance-based constraints tend to lead to distorted aromatic rings and sp² centers. To correct this, the resulting conformations are often minimized with a force field, adding computational complexity and run time. Here we present an alternative strategy that combines the distance geometry approach with experimental torsion-angle preferences obtained from small-molecule crystallographic data. The torsional angles are described by a previously developed set of hierarchically structured SMARTS patterns. The new approach is implemented in the open-source cheminformatics library RDKit, and its performance is assessed by comparing the diversity of the generated ensemble and the ability to reproduce crystal conformations taken from the crystal structures of small molecules and protein–ligand complexes

Better Informed Distance Geometry: Using What We Know To Improve Conformation Generation

Small organic molecules are often flexible, i.e., they can adopt a variety of low-energy conformations in solution that exist in equilibrium with each other. Two main search strategies are used to generate representative conformational ensembles for molecules: systematic and stochastic. In the first approach, each rotatable bond is sampled systematically in discrete intervals, limiting its use to molecules with a small number of rotatable bonds. Stochastic methods, on the other hand, sample the conformational space of a molecule randomly and can thus be applied to more flexible molecules. Different methods employ different degrees of experimental data for conformer generation. So-called knowledge-based methods use predefined libraries of torsional angles and ring conformations. In the distance geometry approach, on the other hand, a smaller amount of empirical information is used, i.e., ideal bond lengths, ideal bond angles, and a few ideal torsional angles. Distance geometry is a computationally fast method to generate conformers, but it has the downside that purely distance-based constraints tend to lead to distorted aromatic rings and sp² centers. To correct this, the resulting conformations are often minimized with a force field, adding computational complexity and run time. Here we present an alternative strategy that combines the distance geometry approach with experimental torsion-angle preferences obtained from small-molecule crystallographic data. The torsional angles are described by a previously developed set of hierarchically structured SMARTS patterns. The new approach is implemented in the open-source cheminformatics library RDKit, and its performance is assessed by comparing the diversity of the generated ensemble and the ability to reproduce crystal conformations taken from the crystal structures of small molecules and protein–ligand complexes

What’s What: The (Nearly) Definitive Guide to Reaction Role Assignment

When analyzing chemical reactions it is essential to know which molecules are actively involved in the reaction and which educts will form the product molecules. Assigning reaction roles, like reactant, reagent, or product, to the molecules of a chemical reaction might be a trivial problem for hand-curated reaction schemes but it is more difficult to automate, an essential step when handling large amounts of reaction data. Here, we describe a new fingerprint-based and data-driven approach to assign reaction roles which is also applicable to rather unbalanced and noisy reaction schemes. Given a set of molecules involved and knowing the product(s) of a reaction we assign the most probable reactants and sort out the remaining reagents. Our approach was validated using two different data sets: one hand-curated data set comprising about 680 diverse reactions extracted from patents which span more than 200 different reaction types and include up to 18 different reactants. A second set consists of 50 000 randomly picked reactions from US patents. The results of the second data set were compared to results obtained using two different atom-to-atom mapping algorithms. For both data sets our method assigns the reaction roles correctly for the vast majority of the reactions, achieving an accuracy of 88% and 97% respectively. The median time needed, about 8 ms, indicates that the algorithm is fast enough to be applied to large collections. The new method is available as part of the RDKit toolkit and the data sets and Jupyter notebooks used for evaluation of the new method are available in the Supporting Information of this publication

Heterogeneous Classifier Fusion for Ligand-Based Virtual Screening: Or, How Decision Making by Committee Can Be a Good Thing

The concept of data fusion - the combination of information from different sources describing the same object with the expectation to generate a more accurate representation - has found application in a very broad range of disciplines. In the context of ligand-based virtual screening (VS), data fusion has been applied to combine knowledge from either different active molecules or different fingerprints to improve similarity search performance. Machine-learning (ML) methods based on fusion of multiple homogeneous classifiers, in particular random forests, have also been widely applied in the ML literature. The heterogeneous version of classifier fusion - fusing the predictions from different model types - has been less explored. Here, we investigate heterogeneous classifier fusion for ligand-based VS using three different ML methods, RF, naı̈ve Bayes (NB), and logistic regression (LR), with four 2D fingerprints, atom pairs, topological torsions, RDKit fingerprint, and circular fingerprint. The methods are compared using a previously developed benchmarking platform for 2D fingerprints which is extended to ML methods in this article. The original data sets are filtered for difficulty, and a new set of challenging data sets from ChEMBL is added. Data sets were also generated for a second use case: starting from a small set of related actives instead of diverse actives. The final fused model consistently outperforms the other approaches across the broad variety of targets studied, indicating that heterogeneous classifier fusion is a very promising approach for ligand-based VS. The new data sets together with the adapted source code for ML methods are provided in the Supporting Information

What’s What: The (Nearly) Definitive Guide to Reaction Role Assignment

When analyzing chemical reactions it is essential to know which molecules are actively involved in the reaction and which educts will form the product molecules. Assigning reaction roles, like reactant, reagent, or product, to the molecules of a chemical reaction might be a trivial problem for hand-curated reaction schemes but it is more difficult to automate, an essential step when handling large amounts of reaction data. Here, we describe a new fingerprint-based and data-driven approach to assign reaction roles which is also applicable to rather unbalanced and noisy reaction schemes. Given a set of molecules involved and knowing the product(s) of a reaction we assign the most probable reactants and sort out the remaining reagents. Our approach was validated using two different data sets: one hand-curated data set comprising about 680 diverse reactions extracted from patents which span more than 200 different reaction types and include up to 18 different reactants. A second set consists of 50 000 randomly picked reactions from US patents. The results of the second data set were compared to results obtained using two different atom-to-atom mapping algorithms. For both data sets our method assigns the reaction roles correctly for the vast majority of the reactions, achieving an accuracy of 88% and 97% respectively. The median time needed, about 8 ms, indicates that the algorithm is fast enough to be applied to large collections. The new method is available as part of the RDKit toolkit and the data sets and Jupyter notebooks used for evaluation of the new method are available in the Supporting Information of this publication