ChEMBL an Open Data Resource of Medicinal Chemistry and Patent Data

The link between biological and chemical worlds is of critical importance in many fields, not least that of healthcare and chemical safety assessment. A major focus in the integrative understanding of biology are genes/proteins and the networks and pathways describing their interactions and functions; similarly, within chemistry there is much interest in efficiently identifying drug-like, cell-penetrant compounds that specifically interact with and modulate these targets. The number of genes of interest is of the range of 10⁵ to 10⁶, which is modest with respect to plausible drug-like chemical space – 10²⁰ to 10⁶⁰. We have built a public database linking chemical structures (~10^^6) to molecular targets (~10^^4), covering molecular interactions and pharmacological activities and Absorption, Distribution, Metabolism and Excretion (ADME) properties – ChEMBL (http://www.ebi.ac.uk/chembl) in an attempt to map the general features of molecular properties and features important for both small molecule and protein targets in drug discovery. We have then used this empirical kernel of data to extend analysis across the human genome, and to large virtual databases of compound structures. Recently we have added large scale text mined chemical structures from patents to our resources (http://www.surechembl.org).

Finding and Applying Multi-Parameter Rules to Guide Successful Drug Discovery

A high quality drug must exhibit a balance of many properties, including potency, ADME and safety.  These are often expressed as property ‘rules’ that a compound must meet in order to progress.  Applying these rules effectively in drug discovery is challenging due to the complex, often conflicting property requirements they reflect, combined with uncertain data because of experimental variability or predictive error. We will discuss how methods known as multi-parameter optimization (MPO) [1] are currently being applied to quickly target compounds with the best chance of success, while avoiding missed opportunities.

But, how do we know what the appropriate profile of property criteria might be to efficiently identify successful leads and candidate compounds for a specific project? The property criteria will depend on the ultimate goal of the project, e.g. therapeutic indication and route of administration, and are typically chosen based on the subjective opinion of the project team.  However, analysis of historical data can help to guide the determination of the most appropriate profile, which can then be used prospectively to prioritise new compounds. We will describe how new methods, known as rule induction [2], can guide this process to identify multi-parameter rules that distinguish successful compounds for a chosen objective. The resulting rules are interpretable and modifiable, allowing experts to understand and adjust them based on their knowledge of the underlying biology and chemistry. Furthermore, the importance of each criterion can be identified, allowing the most important data to be prioritized to make effective compound prioritization decisions.

References: [1] M.D. Segall. Multi-Parameter Optimization: Identifying high quality compounds with a balance of properties. Curr. Pharm. Des. (2012) 18(9) pp. 1292-1310; [2] I. Yusof, F. Shah, N. Greene and M.S. Segall. Finding the Rules for Successful Drug Optimization. Drug. Discov. Today (2014) (in press).

Matched Molecular Pair and Activity Cliffs: the Next Dimension

Matched Molecular Pair (MMP) analysis has become popular as a data driven idea generator for lead optimisation. Existing SAR data is mined for single point changes in structure and their effects on activity: changes that consistently have little effect on activity indicate potential bioisosteric replacements. An adjunct to this approach is to examine the existing SAR on a project to find ‘activity cliffs’, regions where large changes in activity are observed for relatively small changes in structure. However, these methods almost exclusively rely on studying the 2D structures of the molecules concerned rather than the 3D conformation that is involved in binding.

In this paper we will present our research into using 3D methods to detect and interpret activity cliffs. We will show that considering the shape and especially the electrostatic environment around a pair of molecules results in a richer more informed view of the factors causing changes in activity and a hypothesis driven understanding of existing SAR.

Knowledge-Based, Small Molecule Antibody, Design Strategies

Some of the most productive design methods of the past 30 years have been knowledge-based. The best known is surely homology modelling, now a standard tool in most pharmaceutical and agrochemical companies.

The rapid increase in DNA, protein sequence and 3D structure data, so called big data, as well as newer initiatives to put small molecule and bio-assay data into the public domain, have opened up a wealth of new predictive opportunities, but complex commercial systems cannot keep pace with these developments.

Based on our translational research approach,  we take research software from universities and young biotech and license their technology-advanced software directly customers or work with clients to tailor to their specific research efforts.

The presentation will cover:

• knowledge-based fragments
• evolution-seeded drug design
• applications of the Chembl knowledge-base for drug discovery
• antibody humanness score for biomolecule ADME/Tox assessment.

Development of a Structure Generator to Explore a Target Area on Chemical Spaces

On the first stage of development of new drugs, various lead compounds with high activity are required. To design such compounds, we focus on chemical spaces defined by structural descriptors. New compounds close to areas around which highly active compounds exist will show the same degree of activity. Therefore we have been developing a new system of structure generation for searching a target area in chemical spaces. First, highly active compounds are manually selected as initial seeds. Then, those seeds are entered to our generator and structures slightly different from the structures of the seeds are generated and pooled. Next seeds are selected from the new structure pool with the scores based on distance from target on the map. In this study, we used GVK data of ligand-binding affinity to verify the advantage of our generator. Visualization of the chemical space and structure generation were performed, and then the outputs were compared with test data. As a result, our generator could produce many structures similar to the test data, which exist near the target area. This result shows that exploration of the target area on the chemical space was performed.

The Significance of Protein Structure Data Set Choices for in-silico Drug Discovery: Design of BACE1 Inhibitors

In this lecture, I will discuss the significance of protein X-ray crystal structure data set choices for in-silico drug design/screening, using our drug discovery research on BACE1 inhibitors as an example. β-Secretase, also called BACE1 (β-site APP amyloid precursor protein cleaving enzyme 1) is a molecular target for developing Alzheimer’s disease (AD) drugs. BACE1 triggers the formation of amyloid β peptide (Aβ), which is the main component of senile plaques in the brains of AD patients, and is recognized as the causative agent of AD. BACE1 recognizes the EVKM*D sequence and cleaves amyloid precursor protein (APP) on the N-terminal side of the Aβ domain to produce Aβ. Swedish-mutant APP is found in familial AD patients and its cleavage site is mutated to the EVNL*D sequence, which is cleaved faster than the wild-type sequence is by BACE1. Ghosh et al reported the first X-ray crystal structure (1FKN) of BACE1 in a complex with an inhibitor (OM99-2) that was designed based on the Swedish-mutant sequence. The 1FKN structure showed that the Arg235 side chain of BACE1 interacted with the P₂ side chain (Asn) of OM99-2 by hydrogen bonding. Many researchers, including our group, have reported BACE1 inhibitors that are based on the Swedish-mutant sequence and are designed using this crystal structure data set for docking calculation. However, we previously reported that most inhibitors complexed with BACE1, with the exception of OM99-2, and interacted with the Arg235 side chain of BACE1 by quantum chemical interactions such as σ-π interactions and not by hydrogen bonding. Furthermore, I found that such quantum chemical interactions are important for BACE1 inhibition. These findings indicated that the concepts for designing substrates and inhibitors are fundamentally different. Therefore, I proposed an “electron donor/acceptor bioisostere” medicinal science concept based on quantum chemical interactions, and applied it to design the first peptides with BACE1 inhibitory activity.

Linear Expression by Representative Energy Terms: A Novel QSAR Procedure Using Theoretical Computations on Protein–Ligand Complexes

More than half a century has passed since Drs. Hansch and Fujita proposed a general approach to the formulation of QSAR in 1961. Their approach (Hansch-Fujita analysis) has provided a new perspective for chemical-biological interactions as well as a number of successes in drug discovery. Now it is time to develop a new and promising approach based on their QSAR with the aid of modern, powerful molecular calculations.

We have proposed a novel QSAR procedure called Linear Expression by Representative Energy terms (LERE)-QSAR involving molecular calculations such as an ab initio fragment molecular orbital (FMO) and QM/MM (ONIOM)  ones. The first assumption made in formulating the LERE-QSAR equationis that the free-energy terms comprising the overall free-energy change (DG_obs) associated with complex formation are all additive (DG_obs = DG_bind + DG_sol + DG_others).DG_bind and DG_sol are the intrinsic binding interaction free-energy of a ligand with a protein, and the solvation free-energy change associated with complex formation, respectively. DG_others, the sum of free-energy terms other than representative free-energy terms, DG_bind and DG_sol, is assumed to be linear with that of representative free-energy terms(DG_others = β (DG_bind + DG_sol) + const, b < 0). The third assumption is an empirical relation between entropic and enthalpic energy changes accompanied with complex formation (TΔS = α DH + const, a > 0). DG_sol is replaceable by its dominant polar contribution DG_sol^pol, and most of DG_sol^pol comes from the enthalpic contribution. Combining the above three equations yields the following concise expression,

DG_obs = g (DE_bind + DG_sol^pol) + const [g = (1 – a) (1 + b)]

where DE_bind is computable using ab initio MO calculations such as FMO and ONIOM, and DG_sol^pol is with continuum solvation models such as GB (generalized Born), PB (Poisson−Boltzmann), and PCM (polarizable continuum model).

We have demonstrated that the LERE-QSAR procedure can excellently reproduce DG_obs associated with complex formation of a series of ligands with a protein (carbonic anhydrase, MMP, influenza and human neuraminidases).

We will also discuss newly introduced two approaches for estimating the representative energy terms; (1) hybrid estimation of PCM and GB/PB for DG_sol^pol and (2) dispersion−corrected Hartree-Fock method (HF−D) for DE_bind.

Dr. John Overington

John was originally trained as a chemist, then moved on to study structural biology, software development and drug discovery. After parts of his career in large Pharma, and small Biotech, John has most recently joined the EMBL-EBI as a Team Leader with responsibility for a large public domain database of drug discovery data, covering target discovery, lead optimisation and clinical development. John is a Fellow of the Royal Society of Chemistry, and also of the Society of Biology.

Dr. Matt Segall

Matt is CEO of Optibrium. He has a Master of Science in computation from the University of Oxford and a Ph.D. in theoretical physics from the University of Cambridge. As Associate Director at Camitro (UK), ArQule Inc. and then Inpharmatica, he led a team developing predictive ADME models and state-of-the-art intuitive decision-support and visualization tools for drug discovery. In January 2006, he became responsible for management of Inpharmatica’s ADME business, including experimental ADME services and the StarDrop software platform. Following acquisition of Inpharmatica, Matt became Senior Director responsible for BioFocus DPI’s ADMET division and in 2009 led a management buyout of the StarDrop business to found Optibrium, which develops software to intuitively guide decision-making through complex and uncertain data.

Dr. Tim Cheeseright

After a DPhil in Chemistry at the University of Oxford, Tim gained experience as both a medicinal chemist and a molecular modeler at Peptide Therapeutics and Medivir before joining Cresset in 2002. Tim has published widely; details of his publications can be found here.

Dr. Thierry Hanser

After a PhD in Machine Learning Applied to Chemical Reaction Mining at the University of Strasbourg, Thierry completed postdoctoral research in Retro-Synthetic Analysis in Professor E.J. Corey’s group at the University of Harvard and in Denovo Drug Design in Professor Peter Johnson’s group at the University of Leeds.  Thierry joined Lhasa Limited in 2006 and currently works in the research group focussing on automated knowledge discovery and (Q)SAR model building.

Dr. Mark Swindells

2007–Present _ Ebisu Ltd.  (Chemogenomix)

1998–2006 _ Inpharmatica Ltd.

1996–1998 _ Helix Research Institute (Kisarazu) gene, protein, analysis: (TARA project, Mitsuhiko Ikura Lab) University of Tsukuba;RIKEN (Riken, Yokohama) protein three-dimensional structural analysis

1994–1998 – Yamanouchi Pharmaceutical Co., Ltd. Molecular Design Group

1992–1994 – Project of the Ministry of International Trade and Industry, Tanpakukogakukenkyusho, Osaka

1988–1992 – Birkbeck College & University College London

Dr. Kimito Funatsu

1983 _ Ph D: Kyushu University, Department of Chemistry, Faculty of Sciences
1984-1988 _ Assistant Professor, Toyohashi University of Technology, Department of Materials Science
1988-1992 _ Assistant Professor, Toyohashi University of Technology, Department of Knowledge-Based Information Science
1992-2004 _ Associate Professor, Toyohashi University of Technology, Department of Knowledge-Based Information Science
2004 – present _ Professor, The University of Tokyo, Department of Chemical system Engineering

Dr. Yoshio Hamada

1983 _ Master: Tokushima University, Faculty of Pharmaceutical Sciences

1983-1988 _ Lab worker, Wako pure chemical industries, Ltd., Research Laboratory

1988-1989 – Research Associate, Kyoto University, Faculty of Pharmaceutical Sciences

1988-1997 _ Lead Researcher, Toyobo Co., Ltd., Biochemical Department

2004 _ Ph.D.: Kyoto Pharmaceutical University, Faculty of Pharmaceutical Sciences

2004-2010 _ Assistant Professor, Kyoto Pharmaceutical University, 21st Century Center of Excellence program

2011-present _ Research Associate, Kobe Gakuin University, Faculty of Pharmaceutical Sciences

Dr. Hiroshi Chuman

1976 _ BCS; Tokyo University, Department of Agricultural Chemistry

1978 _ MS; Interrelated Sciences, Tokyo University

1989 _ Ph.D.; Agricultural Sciences, Tokyo University

1978−1996 _ Research Director; Kureha Chemical Industry Co. Ltd. (Kureha Corporation)

1997 – Lecturer; Ochanomizu University

1998−present _ Professor of Theoretical Chemistry for Drug Discovery, Institute of Health Biosciences, University of Tokushima, Graduate School

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Cresset

Cresset provides software and consulting services, particularly in the area of drug discovery. Cresset’s patented and proven field technology uses biological activity rather than chemical similarity as the key descriptor of molecules, so customers can create novel bioisosteres and compare compounds across chemical series. With this knowledge customers are empowered to design new molecules that go beyond the obvious iterations to be the best possible step forward for the project. For the business that has made the investment in Cresset’s software or services, the result is motivated, highly productive scientists that deliver compounds with an excellent chance of success.

Cresset社は、創薬分野におけるソフトウェアとコンサルティングサービスを提供しています。Cresset社の特許でもある「フィールド」テクノロジーは分子の主要な記述子として化学的類似性ではなく生物学的活性を利用しており、新たな生物学的等価体の生成や化学シリーズにわたる化合物の比較を行うことができます。この機能により、明らかな繰り返しの先にある、プロジェクトを進めるための大きな一歩となりうる新しい分子を設計することができます。Cresset社のソフトウェアまたはサービスに投資したビジネスでは、優れた科学者が成功の可能性の高い化合物を提供します。Optibrium社は、高品質な医薬品候補の設計と選択を支援する創薬のソフトウェアソリューションを提供しています.

Optibrium

Optibrium provides drug discovery software solutions that bring confidence to the selection and design of high quality candidate drugs. The Company’s flagship platform, StarDrop, creates an intuitive, highly visual and flexible environment to facilitate and speed up lead identification and optimization, quickly targeting effective drug candidates with a high probability of success downstream. Founded in 2009, Optibrium continues to develop StarDrop and research novel technologies to improve the efficiency and productivity of the drug discovery process. Optibrium works closely with its broad range of customers and collaborators, that include leading global pharma companies, biotech and academic groups.

Optibrium社の主力プラットフォームであるStarDropは、リード探索と最適化を促進し、スピードアップさせるための、直感的で、優れたビジュアルを持つフレキシブルな環境を提供し、可能性の高い候補化合物を素早く特定することができます。Optibrium社は、創薬プロセスの効率性と生産性を改善するために、StarDropの開発と新しいテクノロジーの研究を続けています。また、グローバルな製薬会社、バイオテクノロジー、研究機関などの幅広い顧客やパートナーと密接に活動しています。

Lhasa Limited

Lhasa Limited is a not-for-profit organisation that facilitates collaborative data sharing projects in the pharmaceutical, cosmetics and chemistry-related industries. A pioneer in the production of knowledge-based systems for forward thinking scientists, Lhasa Limited continues to draw on over thirty years of experience to create user-friendly, state of the art in silico prediction and database systems. Lhasa Limited believes in ‘Shared Knowledge, Shared Progress’ and its not-for-profit, member driven status is designed to facilitate collaborative working and confidential data sharing between organisations. Lhasa’s software solutions include Derek Nexus and Sarah Nexus for predicting toxicity, Vitic Nexus for managing chemical information, Meteor Nexus for predicting metabolic fate and Zeneth for predicting forced degradation pathways.

Lhasa limitedは製薬、化粧品および化学品メーカー間のデータ共有プロジェクトを促進することを目的とした非営利組織です。進歩的な科学者向けの知識ベースシステム開発の草分けとして、Lhasa Limitedは30年以上に渡り、ユーザーフレンドリーで最先端のin Silico予測システムやデータベースの開発をリードして参りました。Lhasa Limitedは「知識や技術発展の共有」として、ユーザーから非営利目的に情報がもたらされる状況が組織間の共同研究、機密性情報のデータシェアの促進を加速すると考えています。Lhasa Limitedが提供するソフトウェアとして、毒性予測システムDerek NexusおよびSarah Nexus、化合物毒性情報の統合ソリューションVitic Nexus、代謝経路予測のMeteor Nexusや分解経路予測のZenethを取り揃えています。

Chemogenomix

Chemogenomix is a provider of knowledge-based approaches to computational biology, bioinformatics, structural biology and chemogenomics. They  license world class software on behalf of several renowned academic research groups and also offer contract research services. In addition, Chemogenomix  promotes the use of innovative third party methods and help clients to apply them effectively. They also collaborate with their academic partners to deliver new approaches for specific tasks. Chemogenomix’s experience is deep. They work with clients to tackle timely but difficult problems.

過去３０年間、最も生産的なデザイン方法のいくつかはナレッジ・ベースド（知識ベースの方法）です。

最も有名なものは、ホモロジーモデリングであり、現在では多くの医薬と農薬の分野の企業において標準的なツールとなっています。

DNA、タンパク質配列、3D構造データの急激な増加はいわゆるビッグデータとして、また個々の新しい研究テーマから小分子と生物活性データが一般に公開されたことで、新たにナレッジ・ベースド（知識ベース）の方法を適用する機会が増えてきました。しかしながら、商用のシステムは複雑すぎてこのような進展に容易に対応できるようになっておりません。

我々のトランスレーショナル研究から、大学発（ロンドンのUCL、CambridgeのEBIなど) あるいは新進気鋭のバイオテック企業発の高度な研究用ソフトウエアを提供することにより、顧客の個別の研究にニーズに対応することを可能してくれます。