Gregory A. Landrum

Design and NMR-Based Screening of LEF, a Library of Chemical Fragments with Different Local Environment of Fluorine

A novel strategy for the design of a fluorinated fragment library that takes into account the local environment of fluorine is described. The procedure, based on a fluorine fingerprints descriptor, and the criteria used in the design, selection, and construction of the library are presented. The library, named LEF (Local Environment of Fluorine), combined with (19)F NMR ligand-based screening experiments represents an efficient and sensitive approach for the initial fragment identification within a fragment-based drug discovery project and for probing the presence of fluorophilic protein environments. Proper setup of the method, according to described theoretical simulations, allows the detection of very weak-affinity ligands and the detection of multiple ligands present within the same tested mixture, thus capturing all the potential fragments interacting with the receptor. These NMR hits are then used in the FAXS experiments for the fragment optimization process and for the follow-up screening aimed at identifying other chemical scaffolds relevant for the binding to the receptor.

SECONDARY BONDING BETWEEN CHALCOGENS OR PNICOGENS AND HALOGENS

An intimate connection between bonding types that are at times characterized as electron-rich three-center, as hypervalent, as secondary, or as donor–acceptor in the solid state is found in the quasi-linear X-Q-X systems (X=halogen, Q=chalcogen or element of Group 15).

Open-source platform to benchmark fingerprints for ligand-based virtual screening

Similarity-search methods using molecular fingerprints are an important tool for ligand-based virtual screening. A huge variety of fingerprints exist and their performance, usually assessed in retrospective benchmarking studies using data sets with known actives and known or assumed inactives, depends largely on the validation data sets used and the similarity measure used. Comparing new methods to existing ones in any systematic way is rather difficult due to the lack of standard data sets and evaluation procedures. Here, we present a standard platform for the benchmarking of 2D fingerprints. The open-source platform contains all source code, structural data for the actives and inactives used (drawn from three publicly available collections of data sets), and lists of randomly selected query molecules to be used for statistically valid comparisons of methods. This allows the exact reproduction and comparison of results for future studies. The results for 12 standard fingerprints together with two simple baseline fingerprints assessed by seven evaluation methods are shown together with the correlations between methods. High correlations were found between the 12 fingerprints and a careful statistical analysis showed that only the two baseline fingerprints were different from the others in a statistically significant way. High correlations were also found between six of the seven evaluation methods, indicating that despite their seeming differences, many of these methods are similar to each other.

Big Data from Pharmaceutical Patents: A Computational Analysis of Medicinal Chemists’ Bread and Butter

Multiple recent studies have focused on unraveling the content of the medicinal chemists toolbox. Here, we present an investigation of chemical reactions and molecules retrieved from U.S. patents over the past 40 years (1976-2015). We used a sophisticated text-mining pipeline to extract 1.15 million unique whole reaction schemes, including reaction roles and yields, from pharmaceutical patents. The reactions were assigned to well-known reaction types such as Wittig olefination or Buchwald-Hartwig amination using an expert system. Analyzing the evolution of reaction types over time, we observe the previously reported bias toward reaction classes like amide bond formations or Suzuki couplings. Our study also shows a steady increase in the number of different reaction types used in pharmaceutical patents but a trend toward lower median yield for some of the reaction classes. Finally, we found that todays typical product molecule is larger, more hydrophobic, and more rigid than 40 years ago.

Ferromagnetism in Transition Metals: A Chemical Bonding Approach

Band structure calculations from first principles provide the basis of a simple explanation for the appearance of ferromagnetism in iron, cobalt, and nickel that is founded upon a uniquely chemical concept: bonding. It is shown that the onset of ferromagnetism strengthens the metal-metal bonds in these transition metals by reducing the antibonding nearest neighbor interactions that would otherwise appear at the Fermi level.

Similarity maps - a visualization strategy for molecular fingerprints and machine-learning methods

AbstractFingerprint similarity is a common method for comparing chemical structures. Similarity is an appealing approach because, with many fingerprint types, it provides intuitive results: a chemist looking at two molecules can understand why they have been determined to be similar. This transparency is partially lost with the fuzzier similarity methods that are often used for scaffold hopping and tends to vanish completely when molecular fingerprints are used as inputs to machine-learning (ML) models. Here we present similarity maps, a straightforward and general strategy to visualize the atomic contributions to the similarity between two molecules or the predicted probability of a ML model. We show the application of similarity maps to a set of dopamine D3 receptor ligands using atom-pair and circular fingerprints as well as two popular ML methods: random forests and naïve Bayes. An open-source implementation of the method is provided.

MultiMCS: a fast algorithm for the maximum common substructure problem on multiple molecules.

Several efficient correspondence graph-based algorithms for determining the maximum common substructure (MCS) of a pair of molecules have been published in the literature. The extension of the problem to three or more molecules is however nontrivial; heuristics used to increase the efficiency in the two-molecule case are either inapplicable to the many-molecule case or do not provide significant speedups. Our specific algorithmic contribution is two-fold. First, we show how the correspondence graph approach for the two-molecule case can be generalized to obtain an algorithm that is guaranteed to find the optimum connected MCS of multiple molecules, and that runs fast on most families of molecules using a new divide-and-conquer strategy that has hitherto not been reported in this context. Second, we provide a characterization of those compound families for which the algorithm might run slowly, along with a heuristic for speeding up computations on these families. We also extend the above algorithm to a heuristic algorithm to find the disconnected MCS of multiple molecules and to an algorithm for clustering molecules into groups, with each group sharing a substantial MCS. Our methods are flexible in that they provide exquisite control on various matching criteria used to define a common substructure.

Using information from historical high-throughput screens to predict active compounds.

Modern high-throughput screening (HTS) is a well-established approach for hit finding in drug discovery that is routinely employed in the pharmaceutical industry to screen more than a million compounds within a few weeks. However, as the industry shifts to more disease-relevant but more complex phenotypic screens, the focus has moved to piloting smaller but smarter chemically/biologically diverse subsets followed by an expansion around hit compounds. One standard method for doing this is to train a machine-learning (ML) model with the chemical fingerprints of the tested subset of molecules and then select the next compounds based on the predictions of this model. An alternative approach would be to take advantage of the wealth of bioactivity information contained in older (full-deck) screens using so-called HTS fingerprints, where each element of the fingerprint corresponds to the outcome of a particular assay, as input to machine-learning algorithms. We constructed HTS fingerprints using two collections of data: 93 in-house assays and 95 publicly available assays from PubChem. For each source, an additional set of 51 and 46 assays, respectively, was collected for testing. Three different ML methods, random forest (RF), logistic regression (LR), and naïve Bayes (NB), were investigated for both the HTS fingerprint and a chemical fingerprint, Morgan2. RF was found to be best suited for learning from HTS fingerprints yielding area under the receiver operating characteristic curve (AUC) values >0.8 for 78% of the internal assays and enrichment factors at 5% (EF(5%)) >10 for 55% of the assays. The RF(HTS-fp) generally outperformed the LR trained with Morgan2, which was the best ML method for the chemical fingerprint, for the majority of assays. In addition, HTS fingerprints were found to retrieve more diverse chemotypes. Combining the two models through heterogeneous classifier fusion led to a similar or better performance than the best individual model for all assays. Further validation using a pair of in-house assays and data from a confirmatory screen--including a prospective set of around 2000 compounds selected based on our approach--confirmed the good performance. Thus, the combination of machine-learning with HTS fingerprints and chemical fingerprints utilizes information from both domains and presents a very promising approach for hit expansion, leading to more hits. The source code used with the public data is provided.

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, naïve 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 .

Intermolecular interactions between hypervalent molecules: Ph2IX and XF3 (X=Cl, Br, I) dimers

The intermolecular bonding in dimers of the T-shaped hypervalent title compounds is analyzed using a combination of density functional calculations and qualitative arguments. Fragment molecular orbital interaction diagrams lead us to the conclusion that the bonding in these species can be understood using the language of donor-acceptor interactions: mixing between occupied states on one fragment and unoccupied states on the other. There is also a strong electrostatic contribution to the bonding. The calculated strengths of these halogen–halogen secondary interactions are all less than 10 kcal mol-1. There is a very soft potential energy surface for the deformation that makes the bridge in the dimers asymmetrical.