Knowledge-based approaches in the design and selection of compound libraries for drug discovery

Current Opinion in Drug Discovery and Development

Volumn 5, Issue 3, 2002, Pages 400-406

  Viswanadhan, Vellarkad N ^a   Balan, Chenera ^a   Hulme, Christopher ^a   Cheetham, Janet C ^a   Sun, Yaxiong ^a  

^a AMGEN INC   (United States)

Author keywords

ADME; Drug properties; Drug likeness; Drugability; Knowledge based approaches; Library design; Oral absorption

Indexed keywords

COMBINATORIAL LIBRARY; DRUG ABSORPTION; DRUG DISTRIBUTION; DRUG EXCRETION; DRUG INDUSTRY; DRUG INFORMATION; DRUG METABOLISM; DRUG POTENCY; DRUG TOXICITY; REVIEW;

ANIMALS; ARTIFICIAL INTELLIGENCE; DATABASES, FACTUAL; DRUG DESIGN; HUMANS;

EID: 0036589827     PISSN: 13676733     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Review

References (30)

1. Kubinyi H: Combinatorial and computational approaches in structure-based drug design. Curr Opin Drug Discovery Dev (1998) 1:16-27. An excellent overview of various methodologies employed in structure-based drug design (contains 112 references).
2. Lewis RA, Pickett SD, Clark DE: Computer-aided molecular diversity analysis and combinatorial library design. Rev Comput Chem (2000) 16:1-51. A review of molecular diversity analysis and its use in chemical library design, including methods and descriptors commonly employed.
3. Mitchell T, Showell GA: Design strategies for building druglike chemical libraries. Curr Opin Drug Discovery Dev (2001) 4:314-318. A review that briefly covers multiple considerations for designing drug-like libraries, including solubility, permeability, metabolism, toxicity and general drug-likeness.
4. Mason JS, Hermsmeier MA: Diversity assessment. Curr Opin Chem Biol (1999) 3:342-349. A review dealing with low- and high-dimensional metrics and pharmacophores in the assessment of diversity. It briefly discusses the concept of drug-likeness and its importance in library design.
5. Walters WP, Murcko A, Murcko MA: Recognizing molecules with drug-like properties. Curr Opin Chem Biol (1999) 3(4):384-387. This review covers the period between the early 1990s and 1999. Six different methodologies for the assessment of drug-likeness are discussed.
6. Lipinski CA, Lombardo F, Dominy BW, Feeney PJ: Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev (1997) 23:3-25. Classical work by Lipinski and colleagues at Pfizer offers the famous Ro5 criteria based on an analysis of drug permeabilty and solubility using calculated physicochemical properties.
7. Ghose AK, Viswanadhan V, Wendoloski JJ: A knowledge-based approach in designing combinatorial or medicinal chemistry libraries for drug discovery. 1. A qualitative and quantitative characterization of known drug databases. J Combinatorial Chem (1999) 1:55-68. A comprehensive analysis of the CMC database is presented, with a definition of drug-likeness that includes chemical fragment analysis, property ranges and reactivity considerations. Two types of property ranges (acceptable and preferred) are determined from the analysis of known drugs and drug classes.
8. Xu J, Stevenson J: Drug-like index: A new approach to measure drug-like compounds and their diversity. J Chem Inf Comput Sci (2000) 40:1177-1187. An interesting index of drug-likeness based on 25 different properties is presented. However, it appears that the drug database is insufficiently cleaned up, resulting in property ranges too broad to be usefully discriminatory (eg, the range for number of rotatable bonds is I to 35 - far too great a range). However, this should not affect the drugability index calculation as it is based on cluster centers, which will not be affected by outliers.
9. Brown RD, Hassan M, Waldman M: Tools for designing diverse, drug-like, cost-effective combinatorial libraries. In: Combinatorial Library Design and Evaluation. Principles, Software Tools, and Applications in Drug Discovery. Ghose AK, Viswanadhan VN (Eds), Marcel-Dekker, New York, NY, USA (2001):301-335. Tools available in Cerius2 (software from Accelrys) have been utilized to design drug-like, cost-effective Ugi combinatorial libraries. For constraining the reagent selection toward drug-like compounds in the library, property ranges computed from a cleaned up version of WDI (90% cutoff values) are used.
10. Oprea TI, Davis AM, Teague SJ, Leeson PD: Is there a difference between leads and drugs? A historical perspective. J Chem Inf Comput Sci (2001) 41:1308-1315. 96 Lead-drug pairs identified from literature are examined with respect to property profiles, and property ranges are calculated for leads. Leads have tighter ranges than drugs (eg, a lower molecular-weight cutoff of 450 Da for leads versus 500 Da for drugs). The authors argue that at the early stages of hit/lead identification, tighter ranges should be used. We, however, note that hits and leads have different properties, and that synthetic tractability is more pertinent than conformity to property ranges at the stage of hit-to-lead development.
11. Viswanadhan VN, Ghose AK, Kiselyov A, Wendoloski JJ, Weinstein JN: Knowledge-based approaches for the design of small-molecule libraries for drug discovery. In: Combinatorial Library Design and Evaluation. Software Tools, and Applications in Drug Discovery. Ghose AK, Viswanadhan VN (Eds), Marcel-Dekker, New York, NY, USA (2001):267-289. Several factors need to be evaluated in designing a drug-like library. This book chapter identifies these factors, describes a protocol to evaluate them and offers illustrative examples.
12. Oprea TI: Property distribution of drug-related chemical databases. J Comput-Aided Mol Des (2000) 14:251-264. Preferred property ranges of known drugs is obtained for a number of properties, including counts of H-bond donors/acceptors, rotatable bonds and rings.
13. Ajay A, Walters WP, Murcko MA: Can we learn to distinguish between "drug-like" and "nondrug-like" molecules? J Med Chem (1998) 41:3314-3324. A Bayesian neural net model capable of discriminating between drug (CMC) and non-drug (ACD) databases is presented. These authors use both property ranges and substructure keys as descriptors, and demonstrate the generality of the model by correctly classifying 80% MDDR compounds as drug-like.
14. Sadowski J, Kubinyi H: A scoring scheme for discriminating between drugs and nondrugs. J Med Chem (1998) 41:3325-3329. AlogP atom types are used as descriptors for developing a neural net model capable of discriminating drug and non-drug databases. The overall predictive power of this model is similar to the model described in reference [13].
15. Viswanadhan VN, Ghose AK, Revankar GR, Robins RK: Atomic physicochemical parameters for three dimensional structure directed quantitative structure-activity relationships. 4. Additional parameters for hydrophobic and dispersive interactions and their application for an automated superposition of certain naturally occurring nucleoside antibiotics. J Chem Inf Comput Sci (1989) 29:163-172.
16. Sadowski J: Druglikeness profiles of chemical libraries. In: Combinatorial Library Design and Evaluation. Software Tools, and Applications in Drug Discovery. Ghose AK, Viswanadhan VN (Eds), Marcel-Dekker, New York, NY, USA (2001):291-300. A review of the neural net models for assessing drug-likeness is given. A retrospective analysis of HTS data comparing the drug-likeness score at different stages of screening and testing is given. Interestingly, the percentage of compounds with a score > 0.3 remains approximately the same in the initial stages of HTS and increases only at the stage of lead optimization. This indicates that such elaborate neural net models are more useful at the later stage than at the initial hit-finding stage.
17. Wagener M, van Geerestein VJ: Potential drugs and nondrugs: Prediction and identification of important structural features. J Chem Inf Comput Sci (2000) 40:280-292. A decision tree approach using an 'extended atom type' representation of molecular structures offers good predictive power in discriminating drugs and non-drugs. An interesting finding is that drugs are characterized by the presence of polar functionalities such as hydroxyls, amines, carboxyl, phenol and enol groups, whereas non-drugs typically contain aromatic groups and fewer polar functionalities.
18. Godden JW, Bajorath J: Chemical descriptors with distinct levels of information content and varying sensitivity to differences between selected compound databases identified by SE-DSE analysis. J Chem Inf Comput Sci (2002) 42:87-93. An information theoretic approach is developed to identify properties with profiles that are sensitive to differences in the databases (drugs versus non-drugs).
19. Bemis GW, Murcko MA: The properties of known drugs. 1. Molecular frameworks. J Med Chem (1996) 39:2887-2893. A classical work that clearly defines and identifies molecular building blocks from a drug database.
20. Lewell XQ, Judd DB, Watson SP, Hann MM: RECAP - retrosynthetic combinatorial analysis procedure: A powerful new technique for identifying privileged molecular fragments with useful applications in combinatorial chemistry. J Chem Inf Comput Sci (1998) 38:511-522. To generate virtual libraries of synthetically tractable, drug-like compounds, a retrosynthetic combinatorial analysis procedure is developed, applied to WDI database, and a knowledge base is built which stores the drug motifs. These motifs can be used to design libraries by appropriately adding fragments rich in biologically recognized elements.
21. Bemis GW, Murcko MA: Properties of known drugs. 2. Side chains. J Med Chem (1999) 42:5095-5099.
22. Wang J, Ramnarayan K: Toward designing drug-like libraries: A novel computational approach for prediction of drug feasibility of compounds. J Combinatorial Chem (1999) 1:524-533. An approach to quantity drug-likeness by assessing the similarity of chemical environment of each atom (or group of atoms) in a compound to the chemical environments of atoms in known drugs.
23. Sheridan RP: The most common chemical replacements in drug-like compounds. J Chem Inf Comput Sci (2002) 42:103-108. A list of unique fragment pairs that frequently replace each other in similar chemical envoronments is identified from the MDDR database, using a new algorithm. Designing drug-like analogs of a lead will be facilitated by creating a lookup table based on this work.
24. Andrews CW, Bennett L, Yu LX: Predicting human oral bioavailability of a compound: Development of a novel quantitative structure-bioavailability relationship. Pharm Res (2000) 17:639-644.
25. Palm K, Stenberg P, Luthman K, Artursson P: Polar molecular surface properties predict the intestinal absorption of drugs in humans. Pharm Res (1997) 14:568-571. This paper, together with an earlier publication by these authors, identifies dynamic polar surface area as an important molecular characteristic that correlates with in vitro drug transport and oral absorption.
26. Kelder J, Grootenhuis PDJ, Bayada DM, Delbressine LPC, Ploemen J-P: Polar molecular surface as a dominating determinant for oral absorption and brain penetration of drugs. Pharm Res (1999) 16:1514-1519. An important contribution that determines 'allowed' ranges for polar surface area to enable oral absorption or BBB penetration. Consideration of these ranges is an important design aspect for generating drug-like libraries.
27. Egan WJ, Merz KM Jr, Baldwin JJ: Prediction of drug absorption using multivariate statistics. J Med Chem (2000) 43:3867-3877.
28. Rishton GM: Reactive compounds and in vitro false positives in HTS. Drug Disc Today (1997) 2:382-384. This publication is the first attempt to systematically assess reactive functionalities and their role in generating false positives in HTS. Drug-likeness is characterized in part by the absence of reactive functionalities such as those described in this paper.
29. McGovern SL, Caselli E, Grigorieff N, Shoichet BK: A common mechanism underlying promiscuous inhibitors from virtual and high-throughput screening. J Med Chem (2002) 45:1712-1722. This article probes the underlying mechanism for the apparent promiscuity of certain inhibitors identified from virtual and HTS screening. This is an important contribution that will prove useful in reducing the number of false positives from HTS.
30. Richard AM, Williams CR, Cariello NF: Improving structure-linked access to publicly available chemical toxicity information. Curr Opin Drug Discovery Dev (2002) 5:136-143. This review contains a useful compendium of references and website links for gathering data on toxicity of chemicals.