FAF-Drugs3: a web server for compound property calculation and chemical library design

W200–W207 Nucleic Acids Research, 2015, Vol. 43, Web Server issue doi: 10.1093/nar/gkv353 Published online 16 April 2015 FAF-Drugs3: a web server for compound property calculation and chemical library design David Lagorce1,2 , Olivier Sperandio1,2 , Jonathan B. Baell3 , Maria A. Miteva1,2 and Bruno O. Villoutreix1,2,* 1 Université Paris Diderot, Sorbonne Paris Cité, Molécules Thérapeutiques In Silico, Paris 75013, France, 2 Inserm U973, Molécules Thérapeutiques In Silico, Paris 75013, France and 3 Medicinal Chemistry, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria 3052, Australia Received January 28, 2015; Revised March 20, 2015; Accepted April 02, 2015 ABSTRACT INTRODUCTION Chemical biology and even more so drug discovery are challenging endeavors that usually involve high-throughput screening computations and/or experiments, prioritization of the hit compounds and different levels of compound optimization. As such, the nature/composition of the compound collection used in the early phases has a significant * To whom correspondence should be addressed. Tel: +33 1 57 27 83 88; Fax: +33 1 57 27 83 72; Email:  C The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact Drug attrition late in preclinical or clinical development is a serious economic problem in the field of drug discovery. These problems can be linked, in part, to the quality of the compound collections used during the hit generation stage and to the selection of compounds undergoing optimization. Here, we present FAF-Drugs3, a web server that can be used for drug discovery and chemical biology projects to help in preparing compound libraries and to assist decision-making during the hit selection/lead optimization phase. Since it was first described in 2006, FAF-Drugs has been significantly modified. The tool now applies an enhanced structure curation procedure, can filter or analyze molecules with user-defined or eight predefined physicochemical filters as well as with several simple ADMET (absorption, distribution, metabolism, excretion and toxicity) rules. In addition, compounds can be filtered using an updated list of 154 hand-curated structural alerts while Pan Assay Interference compounds (PAINS) and other, generally unwanted groups are also investigated. FAF-Drugs3 offers access to user-friendly html result pages and the possibility to download all computed data. The server requires as input an SDF file of the compounds; it is open to all users and can be accessed without registration at http: //fafdrugs3.mti.univ-paris-diderot.fr. impact in determining both, the quantity and quality of identified hits/leads and ultimately to the overall success of the project (1). There are obviously different ways to prepare a compound collection depending on the disease type, the stage of the project, whether the screening is targetbased or phenotypic-based and the goals (e.g. drug discovery or chemical biology) (2). Numerous rules have been developed over the years to guide the preparation of a compound collection or to select molecules for optimization (3– 5), yet, all these rules, warnings, etc., have to be used with caution as blindly applying such recipes can discard from development many interesting molecules (6–8). The quality of a compound collection can be defined in many different ways but very often, physicochemical properties and the presence of some unwanted chemical groups (e.g. toxic groups or chemicals that interfere with experimental readouts) are used in the field at the beginning of the project. For examples, some rules correlate physicochemical properties with oral administration (like the rule-offive (RO5): molecular mass ≤ 500; calculated log P (cLogP) ≤ 5; number of hydrogen bond donors (HBD) ≤ 5; number of hydrogen bond acceptors (HBA) ≤ 10; a molecule whose properties fell outside these boundaries would be less likely orally absorbed and it was stated that a compound with two parameters out of these ranges would be subject to a flag (3)). Other rules suggest possible toxicity, anticipate difficulties with compound development as well as offtarget interactions, for instance, the GSK 4/400 rule (higher risks of toxicity, interactions with off-targets or difficulties during development if log P > 4 and MW > 400) (9); the Pfizer 3/75 rule (the rule states that a compound has a 6fold reduction in preclinical toxicity when ClogP < 3 and a topological polar surface area (tPSA) > 75 Å2 (and 24fold reduction for basic compounds), the rule is agnostic to the toxicity mechanisms as it is expected that off-target issues are often responsible for the observed toxicity (10)) and the Fsp3 rule (molecular complexity, defined as number of sp3 hybridized carbons/total carbon count) that correlates molecular complexity with success in drug develop- Nucleic Acids Research, 2015, Vol. 43, Web Server issue W201 FAF-Drugs3: SERVICE OVERVIEW AND ENHANCEMENTS Since its first release in 2006 (22), FAF-Drugs has been used by many groups worldwide (more than 30 000 connections) to prepare compound collections or to analyze a small list of chemical compounds. In 2006, FAF-Drugs was designed to perform only physicochemical filtering while the version 2 reported in 2011 (23) was the first free webbased package capable of preparing compound libraries by combining physicochemical rules, undesirable functional group searches and detection of PAINS (18). The major changes introduced in FAF-Drugs3 are: a new input data curation procedure including new ways to search for salts, new ways to predict solubility, optimized computations of properties to for instance predict blood brain barrier penetration or administration by inhalation among others, development of new pre-defined drug-like and lead-like filters, computations of the 3/75 and of the GSK 4/400 rules, search for toxicophores using a hand-curated list of structural alerts, identification of likely protein–protein interaction inhibitors, the prediction of drug-induced phospholipidosis (24), the implementation of the Eli-Lilly open drug discovery medicinal chemistry filter (12) and many new graphical windows such as a chart representing compound complexity. Several selected changes are discussed below. WEB SERVER FAF-Drugs3 is user-centered as it has a new user-friendly interface with new graphical windows that facilitate the analysis of the compounds online. The FAF-Drugs3 web server is an easy-to-use service consisting of a set of seven object-oriented Python modules embedded in the RPBS’ Mobyle framework (25). Mobyle is a centralized workspace for the en (...truncated)