Visualisation and subsets of the chemical universe database GDB-13 for virtual screening

The chemical universe database GDB-13, which enumerates 977million organic molecules up to 13 atoms of C, N, O, S and Cl following simple chemical stability and synthetic feasibility rules, represents a vast reservoir for new fragments. GDB-13 was classified using the MQN-system discussed in the preceding paper for the analysis of PubChem fragments. Two hundred and fifty-five subsets of GDB-13 were generated by the combinatorial use of eight restrictive criteria, including fragment-like ("rule of three”) and scaffold-like (no acyclic carbon atoms) filters. Virtual screening for analogs of 15 commercial drugs of 13 non-hydrogen atoms or less shows that retrieving MQN-neighbors of a query molecule from GDB-13 or its subsets provides on average a 38-fold enrichment in structural analogs (Daylight-type substructure fingerprint Tanimoto T SF>0.7), and a 75-fold enrichment in shape-similar analogs (ROCS TanimotoCombo score>1.4). An MQN-searchable version of GDB-13 is provided at www.gdb.unibe.ch. Graphical Abstrac

Visualisation of the chemical space of fragments, lead-like and drug-like molecules in PubChem

The 4.5 million organic molecules with up to 20 non-hydrogen atoms in PubChem were analyzed using the MQN-system, which consists in 42 integer value descriptors of molecular structure. The 42-dimensional MQN-space was visualised by principal component analysis and representation of the (PC1, PC2), (PC1, PC3) and (PC2, PC3) planes. The molecules were organized according to ring count (PC1, 38% of variance), the molecular size (PC2, 25% of variance), and the H-bond acceptor count (PC3, 12% of variance). Compounds following Lipinski's bioavailability, Oprea's lead-likeness and Congreve's fragment-likeness criteria formed separated groups in MQN-space visible in the (PC2, PC3) plane. MQN-similarity searches of the 4.5 million molecules (see the browser available at www.gdb.unibe.ch ) gave significant enrichment factors for recovering groups of fragment-sized bioactive compounds related to ten different biological targets taken from Chembl, allowing lead-hopping relationships not seen with substructure fingerprint similarity searches. The diversity of different compound series was analyzed by MQN-distance histograms. Graphical Abstrac

Exploring the GDB-13 chemical space using deep generative models

Recent applications of recurrent neural networks (RNN) enable training models that sample the chemical space. In this study we train RNN with molecular string representations (SMILES) with a subset of the enumerated database GDB-13 (975 million molecules). We show that a model trained with 1 million structures (0.1% of the database) reproduces 68.9% of the entire database after training, when sampling 2 billion molecules. We also developed a method to assess the quality of the training process using negative log-likelihood plots. Furthermore, we use a mathematical model based on the “coupon collector problem” that compares the trained model to an upper bound and thus we are able to quantify how much it has learned. We also suggest that this method can be used as a tool to benchmark the learning capabilities of any molecular generative model architecture. Additionally, an analysis of the generated chemical space was performed, which shows that, mostly due to the syntax of SMILES, complex molecules with many rings and heteroatoms are more difficult to sample

Computer Aided Synthesis Prediction to Enable Augmented Chemical Discovery and Chemical Space Exploration

The drug-like chemical space is estimated to be 10 to the power of 60 molecules, and the largest generated database (GDB) obtained by the Reymond group is 165 billion molecules with up to 17 heavy atoms. Furthermore, deep learning techniques to explore regions of chemical space are becoming more popular. However, the key to realizing the generated structures experimentally lies in chemical synthesis. The application of which was previously limited to manual planning or slow computer assisted synthesis planning (CASP) models. Despite the 60-year history of CASP few synthesis planning tools have been open-sourced to the community. In this thesis I co-led the development of and investigated one of the only fully open-source synthesis planning tools called AiZynthFinder, trained on both public and proprietary datasets consisting of up to 17.5 million reactions. This enables synthesis guided exploration of the chemical space in a high throughput manner, to bridge the gap between compound generation and experimental realisation. I firstly investigate both public and proprietary reaction data, and their influence on route finding capability. Furthermore, I develop metrics for assessment of retrosynthetic prediction, single-step retrosynthesis models, and automated template extraction workflows. This is supplemented by a comparison of the underlying datasets and their corresponding models. Given the prevalence of ring systems in the GDB and wider medicinal chemistry domain, I developed ‘Ring Breaker’ - a data-driven approach to enable the prediction of ring-forming reactions. I demonstrate its utility on frequently found and unprecedented ring systems, in agreement with literature syntheses. Additionally, I highlight its potential for incorporation into CASP tools, and outline methodological improvements that result in the improvement of route-finding capability. To tackle the challenge of model throughput, I report a machine learning (ML) based classifier called the retrosynthetic accessibility score (RAscore), to assess the likelihood of finding a synthetic route using AiZynthFinder. The RAscore computes at least 4,500 times faster than AiZynthFinder. Thus, opens the possibility of pre-screening millions of virtual molecules from enumerated databases or generative models for synthesis informed compound prioritization. Finally, I combine chemical library visualization with synthetic route prediction to facilitate experimental engagement with synthetic chemists. I enable the navigation of chemical property space by using interactive visualization to deliver associated synthetic data as endpoints. This aids in the prioritization of compounds. The ability to view synthetic route information alongside structural descriptors facilitates a feedback mechanism for the improvement of CASP tools and enables rapid hypothesis testing. I demonstrate the workflow as applied to the GDB databases to augment compound prioritization and synthetic route design

Développement de méthodes et d’outils chémoinformatiques pour l’analyse et la comparaison de chimiothèques

Some news areas in biology ,chemistry and computing interface, have emerged in order to respond the numerous problematics linked to the drug research. This is what this thesis is all about, as an interface gathered under the banner of chimocomputing. Though, new on a human scale, these domains are nevertheless, already an integral part of the drugs and medicines research. As the Biocomputing, his fundamental pillar remains storage, representation, management and the exploitation through computing of chemistry data. Chimocomputing is now mostly used in the upstream phases of drug research. Combining methods from various fields ( chime, computing, maths, apprenticeship, statistics, etc…) allows the implantation of computing tools adapted to the specific problematics and data of chime such as chemical database storage, understructure research, data visualisation or physoco-chimecals and biologics properties prediction.In that multidisciplinary frame, the work done in this thesis pointed out two important aspects, both related to chimocomputing : (1) The new methods development allowing to ease the visualization, analysis and interpretation of data related to set of the molecules, currently known as chimocomputing and (2) the computing tools development enabling the implantation of these methods.De nouveaux domaines ont vu le jour, à l’interface entre biologie, chimie et informatique, afin de répondre aux multiples problématiques liées à la recherche de médicaments. Cette thèse se situe à l’interface de plusieurs de ces domaines, regroupés sous la bannière de la chémo-informatique. Récent à l’échelle humaine, ce domaine fait néanmoins déjà partie intégrante de la recherche pharmaceutique. De manière analogue à la bioinformatique, son pilier fondateur reste le stockage, la représentation, la gestion et l’exploitation par ordinateur de données provenant de la chimie. La chémoinformatique est aujourd’hui utilisée principalement dans les phases amont de la recherche de médicaments. En combinant des méthodes issues de différents domaines (chimie, informatique, mathématique, apprentissage, statistiques, etc.), elle permet la mise en oeuvre d’outils informatiques adaptés aux problématiques et données spécifiques de la chimie, tels que le stockage de l’information chimique en base de données, la recherche par sous-structure, la visualisation de données, ou encore la prédiction de propriétés physico-chimiques et biologiques.Dans ce cadre pluri-disciplinaire, le travail présenté dans cette thèse porte sur deux aspects importants liés à la chémoinformatique : (1) le développement de nouvelles méthodes permettant de faciliter la visualisation, l’analyse et l’interprétation des données liées aux ensembles de molécules, plus communément appelés chimiothèques, et (2) le développement d’outils informatiques permettant de mettre en oeuvre ces méthodes

The Polypharmacology Browser PPB2: Target Prediction Combining Nearest Neighbors with Machine Learning

Here we report PPB2 as a target prediction tool assigning targets to a query molecule based on ChEMBL data. PPB2 computes ligand similarities using molecular fingerprints encoding composition (MQN), molecular shape and pharmacophores (Xfp), and substructures (ECfp4), and features an unprecedented combination of nearest neighbor (NN) searches and Naïve Bayes (NB) machine learning, together with simple NN searches, NB and Deep Neural Network (DNN) machine learning models as further options. Although NN(ECfp4) gives the best results in terms of recall in a 10-fold cross-validation study, combining NN searches with NB machine learning provides superior precision statistics, as well as better results in a case study predicting off-targets of a recently reported TRPV6 calcium channel inhibitor, illustrating the value of this combined approach. PPB2 is available to assess possible off-targets of small molecule drug-like compounds by public access at ppb2.gdb.tools

Scientific discovery as a combinatorial optimisation problem: How best to navigate the landscape of possible experiments?

A considerable number of areas of bioscience, including gene and drug discovery, metabolic engineering for the biotechnological improvement of organisms, and the processes of natural and directed evolution, are best viewed in terms of a ‘landscape’ representing a large search space of possible solutions or experiments populated by a considerably smaller number of actual solutions that then emerge. This is what makes these problems ‘hard’, but as such these are to be seen as combinatorial optimisation problems that are best attacked by heuristic methods known from that field. Such landscapes, which may also represent or include multiple objectives, are effectively modelled in silico, with modern active learning algorithms such as those based on Darwinian evolution providing guidance, using existing knowledge, as to what is the ‘best’ experiment to do next. An awareness, and the application, of these methods can thereby enhance the scientific discovery process considerably. This analysis fits comfortably with an emerging epistemology that sees scientific reasoning, the search for solutions, and scientific discovery as Bayesian processes

Cheminformatics Tools to Explore the Chemical Space of Peptides and Natural Products

Cheminformatics facilitates the analysis, storage, and collection of large quantities of chemical data, such as molecular structures and molecules' properties and biological activity, and it has revolutionized medicinal chemistry for small molecules. However, its application to larger molecules is still underrepresented. This thesis work attempts to fill this gap and extend the cheminformatics approach towards large molecules and peptides. This thesis is divided into two parts. The first part presents the implementation and application of two new molecular descriptors: macromolecule extended atom pair fingerprint (MXFP) and MinHashed atom pair fingerprint of radius 2 (MAP4). MXFP is an atom pair fingerprint suitable for large molecules, and here, it is used to explore the chemical space of non-Lipinski molecules within the widely used PubChem and ChEMBL databases. MAP4 is a MinHashed hybrid of substructure and atom pair fingerprints suitable for encoding small and large molecules. MAP4 is first benchmarked against commonly used atom pairs and substructure fingerprints, and then it is used to investigate the chemical space of microbial and plants natural products with the aid of machine learning and chemical space mapping. The second part of the thesis focuses on peptides, and it is introduced by a review chapter on approaches to discover novel peptide structures and describing the known peptide chemical space. Then, a genetic algorithm that uses MXFP in its fitness function is described and challenged to generate peptide analogs of peptidic or non-peptidic queries. Finally, supervised and unsupervised machine learning is used to generate novel antimicrobial and non-hemolytic peptide sequences