Richard Lewis

Modern 2D QSAR for drug discovery

2D QSAR is a powerful tool for explaining the relationships between chemical structure and experimental observations. Key elements of the method are the numerical descriptors used to translate a chemical structure into mathematical variables, the quality of the observed data and the statistical methods used to derive the relationships between the observations and the descriptors. There are some caveats to what is essentially a simple procedure: overfitting of the data, domain applicability to new structures and making good error estimates for each prediction. 2D QSAR models are used routinely during the process of optimization of a chemical series towards a candidate for clinical trials. As more knowledge is gained in this area, 2D QSARs will become acceptable surrogates for experimental observations. WIREs Comput Mol Sci 2014, 4:505–522. doi: 10.1002/wcms.1187

Collaborating to improve the use of free-energy and other quantitative methods in drug discovery

In May and August, 2016, several pharmaceutical companies convened to discuss and compare experiences with Free Energy Perturbation (FEP). This unusual synchronization of interest was prompted by Schrödinger’s FEP+ implementation and offered the opportunity to share fresh studies with FEP and enable broader discussions on the topic. This article summarizes key conclusions of the meetings, including a path forward of actions for this group to aid the accelerated evaluation, application and development of free energy and related quantitative, structure-based design methods.

FOCUS — Development of a Global Communication and Modeling Platform for Applied and Computational Medicinal Chemists

Communication of data and ideas within a medicinal chemistry project on a global as well as local level is a crucial aspect in the drug design cycle. Over a time frame of eight years, we built and optimized FOCUS, a platform to produce, visualize, and share information on various aspects of a drug discovery project such as cheminformatics, data analysis, structural information, and design. FOCUS is tightly integrated with internal services that involve-among others-data retrieval systems and in-silico models and provides easy access to automated modeling procedures such as pharmacophore searches, R-group analysis, and similarity searches. In addition, an interactive 3D editor was developed to assist users in the generation and docking of close analogues of a known lead. In this paper, we will specifically concentrate on issues we faced during development, deployment, and maintenance of the software and how we continually adapted the software in order to improve usability. We will provide usage examples to highlight the functionality as well as limitations of FOCUS at the various stages of the development process. We aim to make the discussion as independent of the software platform as possible, so that our experiences can be of more general value to the drug discovery community.

Making sure there's a "give" associated with the "take": producing and using open-source software in big pharma

In contrast to bioinformatics, open-source software is not as widely used in the pharmaceutical industry for molecular modeling and cheminformatics. Typical reasons given for this include problems with code quality, stability, and long-term support for the software (somehow this is less of a concern with bioinformatics software... kind of makes one think). Recently, our group has started making heavy use of an open-source cheminformatics toolkit RDKit [1] in our production environment. Importantly, we are not just acting as consumers of open-source software -- we are active members of the open-source community and have support from management to contribute code back to the project. In this presentation we will provide a brief overview of the RDKit itself and then present a number of case studies of how we have made use of this open-source platform. Examples will include using the toolkit for method development [2,3], integration with proprietary tools, and some recent (and upcoming) contributions to the open-souce community, including a database cartridge for fast and flexible similarity searching in the open-source PostgreSQL database [4], and adding support for the RDKit within the open-source pipelining platform Knime [5]. We will finish with a discussion of some practical aspects of working on and with open-source tools in a large research organization.

Can picolinamide be a promising cocrystal former

Eight novel cocrystals of picolinamide are reported in this study. Lattice energy calculations may help us to understand their relative stabilities versus those of the individual components. The stoichiometry of one cocrystal changed when heated. A polymorph of picolinamide was obtained alongside a cocrystal in a screening experiment.

IADE: a system for intelligent automatic design of bioisosteric analogs

IADE, a software system supporting molecular modellers through the automatic design of non-classical bioisosteric analogs, scaffold hopping and fragment growing, is presented. The program combines sophisticated cheminformatics functionalities for constructing novel analogs and filtering them based on their drug-likeness and synthetic accessibility using automatic structure-based design capabilities: the best candidates are selected according to their similarity to the template ligand and to their interactions with the protein binding site. IADE works in an iterative manner, improving the fitness of designed molecules in every generation until structures with optimal properties are identified. The program frees molecular modellers from routine, repetitive tasks, allowing them to focus on analysis and evaluation of the automatically designed analogs, considerably enhancing their work efficiency as well as the area of chemical space that can be covered. The performance of IADE is illustrated through a case study of the design of a nonclassical bioisosteric analog of a farnesyltransferase inhibitor—an analog that has won a recent “Design a Molecule” competition.

Computational chemistry at novartis

Computational approaches have become an integral part of modern drug discovery and medicinal chemistry. These approaches can be roughly classified into data/information mining (or filtering) and modelling/simulation methods. Taken together, they represent an ever growing source of hypotheses used to guide experimental approaches and hence drug discovery decisions. Therefore, it is not only important to optimally understand and apply existing methods, but also invest in the development of new algorithms to further improve our selection of drug candidate. The present contribution will describe a few approaches which have become routine at Novartis.

Gazing into the crystal ball; the future of computer-aided drug design

Twenty-five years is almost a full career for a scientist, but before looking to the future, we should ask what is really new in the last 25 years, i.e. since 1986? Surprisingly little! Here is a partial but still fairly good list of techniques routinely used by modellers: high throughput docking, high precision docking, free-energy calculations, quantum mechanics, molecular mechanics, distance geometry, molecular dynamics, statistical thermodynamics, conformational searching, scaffold morphing, solvation, QSPR, QSAR, bioavailability predictions, pharmacophores, protein modeling, de novo design, library design, chemical databases and searching, data analysis and visualization, virtual screening, chemometrics, interaction analysis using small molecule and protein x-rays, and FBDD. The majority of these techniques were introduced in the early to mid 1980s, and we think everything on the list except FBDD was introduced by the early 1990s (many techniques have been re-invented since; the collective memory of the literature seems to be under 10 years and falling). The biggest revolution in computational chemistry over the last 25 years was not a new computational technique, but rather the introduction of Beowulf clusters around 2000, which in just a few years increased processing power by about 1009 beyond Moore’s Law for many problems, i.e., it skipped at least a decade. This ‘‘suddenly’’ enabled application of a large number of the techniques from the 1980s to real systems.

Boosting Pose Ranking Performance via Rescoring with MM‐GBSA

In this self‐docking study, we address the so‐called scoring problem. The ‘scoring problem’ is the inability to unambiguously identify biologically the most relevant pose, when the docking score is the main selection criterion. We use the Molecular Mechanics/Generalized Born Surface Area and ChemPLP scoring functions to assess the structure reproduction performance. Heavy‐atom root‐mean‐squared deviation values are used to compare the docked poses with the crystallographic ones. ‘Partial matching’ is introduced. This algorithm captures the visual observation that the majority of a ligand can be well docked, but yet report a root‐mean‐squared deviation value of >2.0 Å. Often this is attributable to arbitrary placements of flexible side chains in undefined solvent regions. The metrics introduced by this algorithm are applicable for assessing the contribution of ligand sampling to the scoring problem. It is shown that rescoring ChemPLP poses with the Molecular Mechanics/Generalized Born Surface Area scoring function improves pose ranking by better discriminating against non‐cognate‐like poses. We conclude that poses should not be retained solely on their ranks, but on the score difference relative to the best‐ranked pose.

Chiral Cliffs: Investigating the Influence of Chirality on Binding Affinity

Chirality is understood by many as a binary concept: a molecule is either chiral or it is not. In terms of the action of a structure on polarized light, this is indeed true. When examined through the prism of molecular recognition, the answer becomes more nuanced. In this work, we investigated chiral behavior on protein–ligand binding: when does chirality make a difference in binding activity? Chirality is a property of the 3D structure, so recognition also requires an appreciation of the conformation. In many situations, the bioactive conformation is undefined. We set out to address this by defining and using several novel 2D descriptors to capture general characteristic features of the chiral center. Using machine‐learning methods, we built different predictive models to estimate if a chiral pair (a set of two enantiomers) might exhibit a chiral cliff in a binding assay. A set of about 3800 chiral pairs extracted from the ChEMBL23 database was used to train and test our models. By achieving an accuracy of up to 75 %, our models provide good performance in discriminating chiral cliffs from non‐cliffs. More importantly, we were able to derive some simple guidelines for when one can reasonably use a racemate and when an enantiopure compound is needed in an assay. We critically discuss our results and show detailed examples of using our guidelines. Along with this publication we provide our dataset, our novel descriptors, and the Python code to rebuild the predictive models.