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The changing landscape of medicinal chemistry optimization

Nature Reviews Drug Discovery volume 24, pages 870–887 (2025)Cite this article

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Abstract

The goal of a small-molecule drug discovery campaign is the development of chemical entities that fulfil the criteria of the target product profile for progression into clinical trials. This objective is realized through multiparameter medicinal chemistry optimization, typically by identifying the compounds at the hit stage with molecular properties that provide a high chance of subsequent success, and then iteratively optimizing the properties, often in parallel, to identify leads and, ultimately, drug candidates. To assess the impact of medicinal chemistry optimizations on molecular properties, a set of new drug candidates reported in the literature between 2015 and 2022, and their corresponding hit and lead compounds, were analysed, and compared with a set of drug candidates identified between 2000 and 2010, and their corresponding hits and leads. This analysis was complemented by similar analyses of the internal medicinal chemistry programmes pursued at AstraZeneca and Novartis. Here, we highlight and discuss the implications of the observed trends, which include shifts in key physicochemical properties and strategic changes in medicinal chemistry programmes.

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Fig. 1: Overview of characteristics of the literature and company compounds.
Fig. 2: Mean values of selected physicochemical parameters of the hit, lead and candidate compounds extracted from the literature and company datasets.
Fig. 3: Overview of the optimization strategies for compounds in the literature and company datasets.
Fig. 4: Potency and similarity characteristics for the literature and company compounds.
Fig. 5: Characteristics of novel-modality candidates.

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Acknowledgements

The authors are grateful for the valuable suggestions and comments of the referees. They thank all members of Global Discovery Chemistry at Novartis who provided information on the hit finding strategies. The authors thank A. Schuffenhauer, D. Baeschlin, J. Duca and M. Mogi for critical feedback on the analyses. They thank all colleagues at AstraZeneca who have provided valuable data for the analysis. A.R., L.M.M. and G.M.K. were supported by the National Drug Discovery and Development Laboratory project (PharmaLab, RRF-2.3.1-21-2022-00015) of the National Research, Development and Innovation Office. A.R. was supported by the Hungarian Academy of Sciences: János Bolyai Research Scholarship.

Author information

Author notes

  1. Levente M. Mihalovits

    Present address: Head Office, Chemaxon Ltd., Budapest, Hungary

  2. Maximilian Beckers

    Present address: dsm-firmenich, Satigny, Switzerland

  3. Nikolaus Stiefl

    Present address: F. Hoffman-La Roche AG, Basel, Switzerland

  4. These authors contributed equally: Anita Rácz, Levente M. Mihalovits.

Authors and Affiliations

  1. National Drug Research and Development Laboratory, Drug Innovation Centre, Research Centre for Natural Sciences, Budapest, Hungary

    Anita Rácz, Levente M. Mihalovits & György M. Keserű

  2. Biomedical Research, Novartis Pharma AG, Basel, Switzerland

    Maximilian Beckers, Nikolas Fechner, Nikolaus Stiefl & Finton Sirockin

  3. Medicinal Chemistry, Oncology R&D, AstraZeneca, The Discovery Centre, Cambridge, UK

    William McCoull

  4. Medicinal Chemistry, Research and Early Development Respiratory and Immunology BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden

    Emma Evertsson

  5. Medicinal Chemistry, Research and Early Development Cardiovascular, Renal and Metabolism BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden

    Malin Lemurell

  6. ChemPass Ltd, Budapest, Hungary

    Gergely Makara

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Correspondence to György M. Keserű.

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Competing interests

M.B., N.F., F.S. and N.S. are or were employees and shareholders of Novartis Pharma. W.M., E.E. and M.L. are employees and shareholders of AstraZeneca. G.M. is a shareholder of ChemPass. G.M.K. is a scientific advisory board member of Cytocast, ChemPass and Molecure Therapeutics and a shareholder of Gedeon Richter.

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Related links

ChEMBL: https://www.ebi.ac.uk/chembl/

Enamine REAL: https://enamine.net/compound-collections/real-compounds/real-database

Instant JChem: https://chemaxon.com/instant-jchem

MarvinSketch: http://www.chemaxon.com

RDKit: Open-Source Cheminformatics: https://www.rdkit.org

TIBCO Statistica: https://www.tibco.com

WuXi AppTec GalaXi: https://wuxibiology.com/drug-discovery-services/hit-finding-and-screening-services/virtual-screening

Supplementary information

Glossary

Chemical fingerprints

Strings representing molecules as a sequence of bits to encode information on their molecular structure.

DNA-encoded libraries

(DELs). A collection of molecules, individually coupled to distinctive DNA tags serving as unique identifiers to identify actives by affinity selection.

Fragments

Small polar organic molecules with reduced complexity and molecular weight (MW).

Lead-likeness

A term typically used for small molecules that resemble leads that could historically be developed into oral drugs. Actual criteria vary but typically lead-like molecules do not have undesirable moieties and have properties that do not violate a stricter version of Lipinski’s rules such as molecular weight (MW) < 350 Da and logP < 3.

Ligand efficiency

(LE). A measure proposed to quantify the average contribution of a heavy atom (HA) to binding (Gibb’s free energy divided by the number of HAs). Originally intended to be used with Kd but since has often been applied to any measure of potency.

Lipinski’s rule of five

(Ro5). Four criteria identified by Lipinski et al. to be relevant for oral bioavailability: molecular weight (MW) < 500 Da, number of hydrogen-bond acceptors (HBAs) < 10, number of hydrogen-bond donors (HBDs) < 5 and clogP < 5.

Lipophilic ligand efficiency

(LLE). Represents the relationship between the target binding (measured as IC50 or EC50) and the lipophilicity (logP or logD) of the compound.

LogD

The distribution coefficient that measures the partition of ionizable compounds as a function of the pH.

LogP

The log of the partition coefficient of a solute between octanol and water.

LogS

The 10-based logarithm of the solubility measured in moles per litre.

Pharmacophore fingerprints

The representation of small-molecule ligands annotated with target interaction features into a string.

pPotency

Calculated as –log(potency), in which potency is either a measure of binding (Kd) or biological activity (Ki, IC50 or EC50).

SMILES strings

(Simplified Molecular Input Line Entry System strings). SMILES is a line notation (a typographical method using printable characters) for entering and representing molecules and reactions.

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Rácz, A., Mihalovits, L.M., Beckers, M. et al. The changing landscape of medicinal chemistry optimization. Nat Rev Drug Discov 24, 870–887 (2025). https://doi.org/10.1038/s41573-025-01225-1

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