The use of 2D fingerprint methods to support the assessment of structural similarity in orphan drug legislation.

In the European Union, medicines are authorised for some rare disease only if they are judged to be dissimilar to authorised orphan drugs for that disease. This paper describes the use of 2D fingerprints to show the extent of the relationship between computed levels of structural similarity for pairs of molecules and expert judgments of the similarities of those pairs. The resulting relationship can be used to provide input to the assessment of new active compounds for which orphan drug authorisation is being sought

Lo-Hi: Practical ML Drug Discovery Benchmark

Finding new drugs is getting harder and harder. One of the hopes of drug discovery is to use machine learning models to predict molecular properties. That is why models for molecular property prediction are being developed and tested on benchmarks such as MoleculeNet. However, existing benchmarks are unrealistic and are too different from applying the models in practice. We have created a new practical \emph{Lo-Hi} benchmark consisting of two tasks: Lead Optimization (Lo) and Hit Identification (Hi), corresponding to the real drug discovery process. For the Hi task, we designed a novel molecular splitting algorithm that solves the Balanced Vertex Minimum $k$-Cut problem. We tested state-of-the-art and classic ML models, revealing which works better under practical settings. We analyzed modern benchmarks and showed that they are unrealistic and overoptimistic. Review: https://openreview.net/forum?id=H2Yb28qGLV Lo-Hi benchmark: https://github.com/SteshinSS/lohi_neurips2023 Lo-Hi splitter library: https://github.com/SteshinSS/lohi_splitterComment: 29 pages, Advances in Neural Information Processing Systems, 202

Assessing and developing methods to explore the role of molecular shape in computer-aided drug design

Shape-based approaches have many potential areas for development in the future for application to in silico pharmacology. Further exploration of the role of molecular shape may lead to better understanding of the substrate specificity of enzymes and the possibility to reduce toxic effects that may be caused by ligands binding to undesired target proteins. Methods exploiting molecular shape for activity and toxicity prediction might have a great influence on the drug discovery process. There are different approaches that might be used for this purpose, e.g. shape fingerprints and shape multipoles. Both methods describe the shape of molecules, discarding any chemical information, using numerical values. Focusing only on shape can lead to identifying novel core structures of molecules, with improved properties. Molecular fingerprints are binary bit strings that encode the structure or shape of compounds; shape is measured indirectly by alignment to a database of standard molecular shapes – the reference shapes. The Shape Database should represent a wide range of possible molecular shapes to produce accurate results. Therefore, this was the main focus of the investigation. The shape multipoles method is a fast computational method to describe the shape of molecules by using only numbers and therefore it requires low storage needs and comparison is performed by simple mathematical operations. To describe the shape, it uses only 13 values (3 quadrupole components and 10 octupole components). The performances of both methods in grouping compounds based on shared biological activity were evaluated using several test sets with slightly better results in case of shape fingerprints. However, the shape multipole approach showed potential in finding differences in shape between enantiomers. Among the possible applications of the shape fingerprints method are solubility prediction (on comparable level as well-established methods) and virtual screening

Semantic distances between medical entities

In this thesis, three different similarity measures between medical entities (drugs) have been implemented. Each of those measures have been computed over one or more dimensions of the drugs: textual, taxonomic and molecular information. All the information has been extracted from the same resource, the DrugBank database

Data exploration and knowledge extraction: their application to the study of endocrine disrupting chemicals

Interest in computer-aided methods for investigating the biological field has increased significantly. One method is Quantitative Structure-Activity Relationships (QSAR), a valuable technique for predicting the effects of a substance from its chemical structure. A challenging application of QSAR is in characterizing the (bio)activity profiles of chemicals. Endocrine disrupters (EDs) are exogenous substances interfering with the function of the endocrine system and represent an interesting field of application for in silico methods. EDs targets include nuclear receptors, particularly effects mediated by the oestrogen receptor (ER). They are also mentioned as substances requiring a more detailed control and specific authorisation within REACH, the new European legislation on chemicals. QSAR represents a challenging method to approach data gap about EDs since REACH substantially boosted interest on computational chemistry to replace experimental testing. This work: aimed to explore the status, availability and reliability of non-testing methods applied to endocrine disruption via oestrogen receptors and eventually to propose new models easily exploitable in regulatory contexts. The work evaluated existing QSAR models present in literature to assess their validity on the basis of the OECD principles for QSAR validation. Different kinds of models have been analysed and they were externally validated with new data found in the literature. Furthermore, new QSAR binary classifiers have been developed using different data mining techniques (e.g.: classification trees, fuzzy logic, neural networks) based on a very large and heterogeneous dataset of chemical compounds. The focus was given to both binding (RBA) and transcriptional activity (RA) better to characterize the effects of EDs. A possible combination of the models was also explored. A very good accuracy was achieved for both RA and RBA (>85%). These models can be a valuable complement to in vivo and in vitro studies in the toxicological characterisation of chemical compounds

Chemical Information Bulletin

Created as a supplement for "the regular journals of the American Chemical Society," this publication contains annotated bibliographies of chemical documentation literature as well as information about meetings, conferences, awards, scholarships, and other news from the American Chemical Society (ACS) Division of Chemical Information (CINF)

Structure-based drug design for the discovery of new treatments for trypanosomiasis

Human African trypanosomiasis (HAT) and Chagas disease are caused by infection with the protozoan parasites Trypanosoma brucei and T. cruzi, respectively. There has historically been a lack of investment into measures to control these diseases. As a result, few drugs are available to treat HAT and Chagas disease, and there is an urgent need for novel alternatives. The enzyme L-threonine 3-dehydrogenase (TDH) initiates the conversion of L-threonine into acetyl-coenzyme A and glycine. This pathway has been shown to play a vital role in T. brucei, particularly in fatty acid synthesis. Exposure of T. brucei in culture to a potent TDH inhibitor, has been shown to be lethal(1) and dual blockade of the TDH pathway and a second pathway for acetyl-coenzyme A production, terminated by pyruvate dehydrogenase, completely inhibits the growth of T. brucei(2,3). Multiple three-dimensional structures of TDH, alone and in complex with ligands, were determined by X-ray crystallography. In parallel, enzyme assays were carried out to investigate the kinetic behaviour of TDH and the modes of action of known TDH inhibitors. The structural information on TDH was used in a virtual screen to predict the binding interactions between the enzyme and a library of around 3000 ligands. These ligands were mainly selected for their diversity and for their inhibition of proteins related to TDH. Subsequently, an in vitro screen was performed to test compounds identified by virtual screening, along with small molecules and fragments from commercial libraries. In total, 27 compounds were identified as TDH inhibitors. Of these compounds, four were found to potently inhibit T. brucei growth. This study has demonstrated the effectiveness of combining structural and functional data in rational drug discovery. Novel aspects of TDH have been discovered, in addition to novel inhibitors that will aid in the design of a new class of antitrypanosomal drugs