Draft:David Alan Winkler

Winkler earned a Bachelor of Science in Chemistry and Chemical Engineering from Monash University between 1970 and 1972, followed by a BAppSci in Applied Physics with Distinction from the Royal Melbourne Institute of Technology in 1974. He completed a first-class honours degree in Chemistry (1975) and a PhD in Chemical Physics and Radioastronomy (1980) at Monash University. His PhD was examined by Nobel Laureate Sir Harry Kroto.

Winkler began his postdoctoral career with Prof. Peter Andrews at the Victorian College of Pharmacy (now Monash Institute of Pharmaceutical Sciences, MIPS) where he wrote some of the first software to design drugs. He then worked a a Research Scientist then Senior Research Scientist and the Defence Science and Technology Organisation (DSTO) in Salisbury, a suburb of Adelaide. He moved back to Melbourne in 1983 as a Senior Research Scientist at the Commonwealth Scientific and Industrial Research Organziation (CSIRO) Division of Applied Organic Chemistry, conducting some of the early research on the application of computational chemistry to the design of bioactive agents and materials. He stayed with CSIRO (the Division undergoing several name changes to Chemicals and Polymers, Molecular Science, Manufacturing amongst others) from 1983 to 2017 in increasingly senior roles, including Senior Principal Research Scientist and Research Affiliate at the Biomolecular Research Institute (BRI). He was a key member of the Dunlena joint venture between CSIRO and Du Pont Crop Protection chemicals, a Syndicated R&D project developing hepatitis B drugs with Macquarie Bank, a major research collaboration with Schering Plough Animal Health on anthelmintics, design of corrosion inhibitors for the Boeing Corporation, design of radioprotectants with Sirtex Medical and the Peter McCallum Cancer Institute, amongst others. He provided valuable intellectual property for the antimicrobial startup company, Betabiotics, antihypertensive and antifibrotics agents for Vectus Biosystems, and stem cell markers for Asymmetrex Inc. From the early 1990s, he was leader in developing of, and applying machine learning methods to the design of drug, agrochemicals, nanomaterials, materials for energy, environment, and corrosion control.

After leaving CSIRO in east 2017, he was appointed Professor of Biochemistry and Chemistry at the La Trobe Institute for Molecular Science (LIMS) at la Trobe University, and is an adjunct professor of Medicinal Chemistry at MIPS, and a Visiting Professor in Pharmacy at the University of Nottingham. His later research spans AI and machine learning in chemistry, systems biology, cheminformatics, nanotoxicology, and drug and materials design.

Winkler has authored over 350 peer-reviewed publications and book chapters and 25 filed patents, with his work frequently cited in scientific literature, government policy, and patents.

Winkler has developed several technologies with commercial impact, including drug design platforms and biomaterials. His work has led to the creation or market flotation of companies such as Betabiotics, Asymmetrex, and Vectus Biosystems.

He has advised governments and international agencies on nanotechnology and public health risks and served on scientific advisory boards for numerous conferences and organizations globally.

Winkler has received numerous prestigious awards, including:

• CSIRO Medal for Business Excellence for the research Alliance with Schering Plough Animal Health (2003)
• CSIRO “One-CSIRO” Award for complex systems science research (2005)
• Newton Turner Fellowship for Exceptional Senior Scientists (2009)
• Royal Australian Chemical Institute, Adrien Albert Award for Medicinal Chemistry (2013)
• Herman Skolnik Award, American Chemical Society (2016)
• Distinguished Fellowship, Royal Australian Chemical Institute (2017)
• AMMA Medal, Association of Molecular Modellers of Australia (2021)
• Federation of Asian Chemical Societies Fellowship (2023)

He has served in professional leadership roles such as:

• Chairman, Royal Australian Chemical Institute Board (2003-2007)
• Member, Australian Academy of Science, National Committee for Chemistry (2012-2015)
• Director, Science and Technology Australia (STA) Board (Chemistry Cluster representative) (2011-2013)
• Subcommittee Member: UIPAC Project No. 2010-057-3-700: Update of Glossary Terms used in Computational Drug Design (2011-2013)
• President, Asian Federation for Medicinal Chemistry (2007-2009)
• President, Federation of Asian Chemical Societies (2017-2019)
• President, Association of Molecular Modellers of Australia (2020)
• Editorial board member and associate editor for journals including ChemMedChem, Advanced Intelligent Systems, and Computational and Structural Biotechnology Journal

He is also a frequent reviewer for top journals like Nature, Angewandte Chemie, JACS, and major research grant bodies worldwide.

Winkler has been featured in international media outlets for his work on artificial intelligence, nanotechnology, and COVID-19 research. His interviews have appeared in The Telegraph, Sky News, COSMOS Magazine, and various national radio and television programs.

Winkler has contributed to major works in medicinal chemistry and machine learning, including book chapters in:

• Bayesian Regularization of Neural Networks, in Artificial Neural Networks: Methods and Applications, Livingston, D (Ed.), Methods in Molecular Biology, Vol. 458 Humana Press, Totowa, NJ 07512 USA 2009, ISBN: 978-1-58829-718-1 pp 25-44 (2009)
• Rational repurposing of drugs, clinical trials candidates, and natural products for SARS-CoV-2 therapy, in Frontiers of COVID-19: Scientific and Clinical Aspects of the Novel Coronavirus 2019, Sasan Adibi, Abbas Rajabifard, Shariful Islam, Alireza Ahmadvand (eds.), Springer Nature 2022. Chapter 23, pp471-487. ISBN 978-3-031-08044-9. (2019)
• Computational approaches, In Adverse Effects of Engineered Nanoparticles, Fadeel, Pietroiusti, and Shvedova (Eds.), Second Edition, Academic Press, London 2017; pp. Chapter 4. ISBN 978-0-12-809199-9; ebook 978-0-12-809490-7 (2017)
• ''Biomaterials discovery: experimental and computational approaches in Tissue Engineering 3rd Edition (C.A. Van Blitterswijk, J. de Boer Ed.), Academic Press, Oxford. November 11, 2022. Chapter 9. ISBN: 9780128244593
• In-silico Structure-based Vaccine Design in Computational Vaccine Design, (Reche, P.A., Ed.) Methods in Molecular Biology 2023. Pp. 371-399. https://www.springer.com/series/7651. ISBN: 1071632388. (2023)

Key journal publications include:

• Winkler, D.A.; Mol, A.; Ozcan, C.; Wurger, T.; Feiler, C.; Hughes, A.; Lamaka, S. Impact of inhibition mechanisms, automation, and computational models on the discovery of organic corrosion inhibitors, Prog. Mater. Sci. 2025, 149, 101392. https://doi.org/10.1016/j.pmatsci.2024.101392.
• Xuying Li; Haoxin Mai; Junlin Lu; Xiaoming Wen; Tu C. Le; Salvy P. Russo; David A. Winkler; Dehong Chen; Rachel A. Caruso, Rational Atom Substitution to Obtain Efficient, Lead-Free Photocatalytic Perovskites Assisted by Machine Learning and DFT Calculations, Angew. Chem. Int. Ed. 2023, e202315002.
• Yan, X.; Yue, T.; Yin, Y.; Hao Zhu, H.; Jiang, G.; Yan, B., Winkler, D.A. Nanotoxicology Data to Information using Artificial Intelligence and Simulation, Chem. Rev, 2023 123(13), 8575-8637.
• Surmiak, M.A.; Meftahi, N.; O Fürer, S.O.; Rietwyk, K.J.; Lu, K.; Ruiz Raga, S.; Evans, C.; Michalska, M.; Deng, H.; McMeekin, D.P.; Alan, T.; Angmo, D.; Vak, D.; Chesman, A.; Christofferson, A.J.; Winkler, D.A.; Russo, S.; Bach, U. Machine Learning-Enhanced High-Throughput Fabrication and Optimization of Quasi-2D Ruddlesden-Popper Perovskite Solar Cells. Adv. Energy Mater. 2023, 2203859. https://doi.org/10.1002/aenm.202203859. Cover.
• Mai, H.; Le, T.C; Chen, D.; Winkler, D.A.; Caruso, R.A. Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery, Chem. Rev. 2022 122(16), 13478-13515. Most popular article from Chemical Reviews.
• Rengasamy, D.; Mase, J.M.; Torres Torres, M.; Rothwell, B.; Winkler, D.A.; Figueredo, G.P. Feature Importance in Machine Learning Models: A Fuzzy Information Fusion Approach, Neurocomput., 2022, 511, 163-174.
• Muratov, E.; Brown, N.; Fourches, D.; Kozakov, D.; Medina-Franco, J.L.; Merz, K.; Isayev, O.; Oprea, T.; Poroikov, V.; Varnek, A.; Winkler, D.A.; Zakharov, A.; Cherkasov, A.; Tropsha, A. A critical overview of computational approaches employed for COVID-19 drug discovery. Chem. Soc. Rev., 2021, 50, 9121-9151.
• Piplani, S.; Singh, P.K.; Winkler, D.A.; Petrovsky, N. In silico comparison of SARS-CoV-2 virus spike protein-ACE2 binding affinities across species; significance for animal susceptibility and viral origin, Sci Rep., 2021, 11, 13063.
• Meftahi, N.; Klymenko, M.; Christofferson, A.J.; Bach, U.; Winkler, D.A.; Russo., S.P. Machine Learning Property Prediction for Organic Photovoltaic Devices, Nat. Computat. Mater., 2020, 6: 166 (2020).
• Muratov, E.N.; Bajorath, J.; Sheridan, R.P.; Tetko, I.; Filimonov, D.; Poroikov, V.; Oprea, T.; Igor Baskin, I.; Varnek, A.; Roitberg, A.; Isayev, O.; Curtalolo, S.; Fourches, D.; Cohen, Y.; Aspuru-Guzik, A.; Winkler, D.A. Dimitris Agrafiotis, D.; Artem Cherkasov, A.; Tropsha, A. QSAR without Borders, Chem. Soc. Rev. 2020, 49, 3525-3564. DOI:10.1039/D0CS00098A.
• Yang, X.; Yang, Q.; Puxty, G; Rees, R; Winkler, D.A. Computational Modelling and Simulation of CO2 Capture by Aqueous Amines, Chem. Rev. 2017, 117 (14), 9524–9593.
• Le, T.C; Winkler, D.A. Discovery and optimization of materials using evolutionary approaches. Chem. Rev. 2016;116 (10), 6107–6132.
• Winkler, D.A.; Thornton, A.; Farjot, G.; Katz, I. The Diverse Biological Properties of the Chemically Inert Noble Gases, Pharm. Ther. 2016; 160:44-64. See also comment on Atlas of Science http://atlasofscience.org/the-inert-noble-gases-are-anything-but-inert-biologically/
• Alexander, D.; Tropsha, A.; Winkler, D.A. Beware of R2: correct statistical usage in QSAR and QSPR studies, J. Chem. Inf. Model. 2015; 55(7): 1316–1322.
• Huh, Y.H.; Noh, M.; Burden, F.R.; Chen, J.C.; Winkler, D.A.; Sherley, J.L. Sparse Feature Selection Identifies H2A.Z as a Novel, Pattern-Specific Biomarker for Asymmetrically Self-Renewing Distributed Stem Cells Stem Cell Res., 2015; 14: 144–154.
• Autefage, H.; Gentleman, E.; Winkler, D.A.: Burden, F.R.; Stevens, M. Sparse feature selection methods identify unexpected global cellular response to strontium-containing materials. Proc. Natl. Acad. Sci. USA 2015; 112(14):4280-4285.
• Celiz, A. D.; Smith, J. G. W.; Langer, R.; Anderson, D. G.; Barrett, D. A.; Young, L. E.; Winkler, D.A.; Davies, M. C.; Denning, C.; Alexander M. R. Materials for the Stem Cell Factories of the Future. Nature Mater. 2014; 13:570-579.
• Le, T.C.; Epa, V.C.; Burden, F.R.; Winkler, D.A. Quantitative Structure-Property Relationship Modeling of Diverse Materials Properties. Chem. Rev. 2012; 112 (5): 2889–2919.
• Epa, V.C.; Burden, F.R.; Tassa, C.; Weissleder, R.; Shaw, S.; Winkler, D.A. Modelling biological activities of nanoparticles. Nano Lett. 2012; 12: 5808−5812.
• Ung, P.; Winkler, D.A. Tripeptide motifs in biology as drug targets, J. Med. Chem. (Persp.) 2011; 54: 1111–1125.

A full list of publications is available on his [https://scholar.google.com.au/citations?user=DePT_VYAAAAJ&hl=en Google Scholar], [https://orcid.org/0000-0002-7301-6076 ORCID], and [https://www.scopus.com/authid/detail.uri?authorId=7102950474 Scopus] profiles.

• [https://scholar.google.com.au/citations?user=DePT_VYAAAAJ&hl=en Google Scholar]
• [https://orcid.org/0000-0002-7301-6076 ORCID]
• https://scholars.latrobe.edu.au/dwinkler
• https://www.eoas.info/biogs/P004737b.htm
• https://research.monash.edu/en/persons/david-winkler
• https://trove.nla.gov.au/people/1476378?c=people
• https://www.linkedin.com/in/david-winkler-767375/?originalSubdomain=au
• [https://fediscience.org/@drdavewinkler Mastodon]

:Category:Australian chemists

:Category:Living people

:Category:1952 (living people)