Lead-like, drug-like or “Pub-like”: how different are they?

Academic and industrial research continues to be focused on discovering new classes of compounds based on HTS. Post-HTS analyses need to prioritize compounds that are progressed to chemical probe or lead status. We report trends in probe, lead and drug discovery by examining the following categories of compounds: 385 leads and the 541 drugs that emerged from them; “active” (152) and “inactive” (1488) compounds from the Molecular Libraries Initiative Small Molecule Repository (MLSMR) tested by HTS; “active” (46) and “inactive” (72) compounds from Nature Chemical Biology (NCB) tested by HTS; compounds in the drug development phase (I, II, III and launched), as indexed in MDDR; and medicinal chemistry compounds from WOMBAT, separated into high-activity (5,784 compounds with nanomolar activity or better) and low-activity (30,690 with micromolar activity or less). We examined Molecular weight (MW), molecular complexity, flexibility, the number of hydrogen bond donors and acceptors, LogP—the octanol/water partition coefficient estimated by ClogP and ALOGPS), LogSw (intrinsic water solubility, estimated by ALOGPS) and the number of Rule of five (Ro5) criteria violations. Based on the 50% and 90% distribution moments of the above properties, there were no significant difference between leads of known drugs and “actives” from MLSMR or NCB (chemical probes). “Inactives” from NCB and MLSMR were also found to exhibit similar properties. From these combined sets, we conclude that “Actives” (569 compounds) are less complex, less flexible, and more soluble than drugs (1,651 drugs), and significantly smaller, less complex, less hydrophobic and more soluble than the 5,784 high-activity WOMBAT compounds. These trends indicate that chemical probes are similar to leads with respect to some properties, e.g., complexity, solubility, and hydrophobicity.

[1]  Lawrence X. Yu,et al.  A provisional biopharmaceutical classification of the top 200 oral drug products in the United States, Great Britain, Spain, and Japan. , 2006, Molecular pharmaceutics.

[2]  Jürgen Drews,et al.  Innovation deficit revisited: reflections on the productivity of pharmaceutical R&D , 1998 .

[3]  Tudor I. Oprea,et al.  Rapid Evaluation of Synthetic and Molecular Complexity for in Silico Chemistry , 2005, J. Chem. Inf. Model..

[4]  F. Lombardo,et al.  Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings , 1997 .

[5]  Tudor I. Oprea,et al.  Is There a Difference between Leads and Drugs? A Historical Perspective , 2001, J. Chem. Inf. Comput. Sci..

[6]  T. Insel,et al.  NIH Molecular Libraries Initiative , 2004, Science.

[7]  Igor V. Tetko,et al.  Prediction of n-Octanol/Water Partition Coefficients from PHYSPROP Database Using Artificial Neural Networks and E-State Indices , 2001, J. Chem. Inf. Comput. Sci..

[8]  Tudor I. Oprea,et al.  WOMBAT: World of Molecular Bioactivity , 2005 .

[9]  Neera Jain,et al.  Prediction of Aqueous Solubility of Organic Compounds by the General Solubility Equation (GSE) , 2001, J. Chem. Inf. Comput. Sci..

[10]  Tudor I. Oprea Current trends in lead discovery: Are we looking for the appropriate properties? , 2002, J. Comput. Aided Mol. Des..

[11]  Andrew R. Leach,et al.  Molecular Complexity and Its Impact on the Probability of Finding Leads for Drug Discovery , 2001, J. Chem. Inf. Comput. Sci..

[12]  Tudor I. Oprea,et al.  Property distribution of drug-related chemical databases* , 2000, J. Comput. Aided Mol. Des..

[13]  Andrew M. Davis,et al.  Design kombinatorischer Leitstruktur‐Bibliotheken , 1999 .

[14]  G. Poda,et al.  Application of ALOGPS 2.1 to predict log D distribution coefficient for Pfizer proprietary compounds. , 2004, Journal of medicinal chemistry.

[15]  Martyn G. Ford,et al.  Simultaneous prediction of aqueous solubility and octanol/water partition coefficient based on descriptors derived from molecular structure , 2001, J. Comput. Aided Mol. Des..

[16]  P. Leeson,et al.  A comparison of physiochemical property profiles of development and marketed oral drugs. , 2003, Journal of medicinal chemistry.

[17]  T I Oprea,et al.  Cheminformatics: a tool for decision-makers in drug discovery. , 2001, Current opinion in drug discovery & development.

[18]  J. Proudfoot Drugs, leads, and drug-likeness: an analysis of some recently launched drugs. , 2002, Bioorganic & medicinal chemistry letters.

[19]  A. Leo CALCULATING LOG POCT FROM STRUCTURES , 1993 .

[20]  I. Tetko,et al.  Application of ALOGPS to predict 1-octanol/water distribution coefficients, logP, and logD, of AstraZeneca in-house database. , 2004, Journal of pharmaceutical sciences.

[21]  Igor V. Tetko,et al.  Virtual Computational Chemistry Laboratory – Design and Description , 2005, J. Comput. Aided Mol. Des..

[22]  D. Lipman,et al.  National Center for Biotechnology Information , 2019, Springer Reference Medizin.

[23]  D F Horrobin,et al.  Innovation in the pharmaceutical industry , 2000, Journal of the Royal Society of Medicine.