papers | scholar | code | che@ur | book | class | talk

note: andrew is currently on sabbatical

research	large language models, chemistry deep learning, molecular dynamics
phd	jorge medina <jmedina9@ur.rochester.edu>, sam cox <swrig30@ur.rochester.edu>, shane smictavy <smichtav@che.rochester.edu>, quintina campbell <qcampbe2@ur.rochester.edu>
postdoc	mayk caldas <mcaldasr@ur.rochester.edu>
pi	andrew white <andrew.white@rochester.edu>, he/him
bio	Andrew White is a researcher with over 50 peer-reviewed publications and books across the domains of large language models in chemistry, explainable artificial intelligence, statistical mechanics, and chemical engineering. He has won junior investigator awards from the National Science Foundation and National Institutes of Health along with professional and teaching awards for excellence as a chemical engineer. Andrew is an active member of the scientific community as a peer reviewer for over 30 journals, multiple national and private grant awarding institutions, and serves on the Chemical Sciences Roundtable at the National Academy of Science. Andrew is also a science communicator with large followings on X and LinkedIn and has been interviewed in multiple publications such as the New York Times, Bloomberg, Nature, Financial Times, and Science. Andrew serves on multiple scientific advisory boards across biotech. He has contributed to the ongoing debate around safety of artificial intelligence as a GPT-4 red teamer, speaking at multiple policy summits, and visiting the White House to advise multiple agencies.
twitter	@andrewwhite01, @SamCox822, @MichtavyShane, @maykcaldas, @quinnycampbell, @4everstudent95
media coverage	interviewed/discussed in MIT Tech Review, Nature, New Scientist, Financial Times, Nature Careers
acknowledgements	doe bes de-sc0023354, nsf cbet #1751471, nsf dge #1922591, nsf dmr #2103553, nih #R35GM137966. previous: nsf che #1764415, nsf iis #2029095
alumni	ziyue yang <@ZiyueYang37>, geemi wellawate <@GWellawatte>, mehrad ansari <@MehradAnsari>, heta gandhi <@gandhi_heta>, rainier barrett <@Rainier_B>

🧪.🐕 | ⚗️💤 | ⚡ | 🥳⚗️ | 🥳🧮 | 📎⚗️ | 🐦🎥 | 🌶️🌊 | ⚗️☁️ | 🧪🌊 | 0xe4cc

---

papers

1. Ansari, M. & White, A. D. Learning peptide properties with positive examples only. Digital Discovery 3, 977–986 (2024).
   tweet | link | pdf | code
2. Ramos, M. C. & White, A. D. Predicting small molecules solubility on endpoint devices using deep ensemble neural networks. Digital Discovery 3, 786–795 (2024).
   tweet | link | pdf | app | code
3. Cox, S., Hammerling, M., Lála, J., Laurent, J., Rodriques, S., Rubashkin, M. & White, A. WikiCrow: Automating synthesis of human scientific knowledge (2024).
   tweet | link | code
4. Ramos, M. C., Collison, C. J. & White, A. D. A review of large language models and autonomous agents in chemistry. arXiv preprint arXiv:2407.01603 (2024).
   tweet | link | pdf | code
5. Laurent, J. M., Janizek, J. D., Ruzo, M., Hinks, M. M., Hammerling, M. J., Narayanan, S., Ponnapati, M., White, A. D. & Rodriques, S. G. LAB-bench: Measuring capabilities of language models for biology research. arXiv preprint arXiv:2407.10362 (2024).
   tweet | link | pdf | code
6. Ansari, M. & White, A. D. Serverless prediction of peptide properties with recurrent neural networks. Journal of Chemical Information and Modeling 63, 2546–2553 (2023).
   tweet | link | pdf | app | code
7. Wellawatte, G. P., Gandhi, H. A., Seshadri, A. & White, A. D. A perspective on explanations of molecular prediction models. Journal of Chemical Theory and Computation 19, 2149–2160 (2023).
   tweet | link | pdf | code
8. White, A. D., Hocky, G. M., Gandhi, H. A., Ansari, M., Cox, S., Wellawatte, G. P., Sasmal, S., Yang, Z., Liu, K., Singh, Y., et al. Assessment of chemistry knowledge in large language models that generate code. Digital Discovery 2, 368–376 (2023).
   tweet | link | pdf | code
9. Wellawatte, G. P., Hocky, G. M. & White, A. D. Neural potentials of proteins extrapolate beyond training data. The Journal of Chemical Physics 159, (2023).
   tweet | link | pdf
10. Bran, A. M., Cox, S., Schilter, O., Baldassari, C., White, A. D. & Schwaller, P. ChemCrow: Augmenting large-language models with chemistry tools. arXiv preprint arXiv:2304.05376 (2023).
    tweet | link | pdf | code | outputs
11. Ramos, M. C., Michtavy, S. S., Porosoff, M. D. & White, A. D. Bayesian optimization of catalysts with in-context learning. arXiv preprint arXiv:2304.05341 (2023).
    tweet | link | pdf | code
12. Medina, J. & White, A. D. Bloom filters for molecules. Journal of Cheminformatics 15, 95 (2023).
    tweet | link | pdf | code
13. Campbell, Q. L., Herington, J. & White, A. D. Censoring chemical data to mitigate dual use risk. arXiv preprint arXiv:2304.10510 (2023).
    tweet | link | pdf | code
14. Medina, J. & White, A. D. Active learning in symbolic regression performance with physical constraints. arXiv preprint arXiv:2305.10379 (2023).
    tweet | link | pdf | code
15. White, A. D. The future of chemistry is language. Nature Reviews Chemistry 7, 457–458 (2023).
    tweet | link | pdf
16. Jablonka, K. M., Ai, Q., Al-Feghali, A., Badhwar, S., Bocarsly, J. D., Bran, A. M., Bringuier, S., Brinson, L. C., Choudhary, K., Circi, D., et al. 14 examples of how LLMs can transform materials science and chemistry: A reflection on a large language model hackathon. Digital Discovery 2, 1233–1250 (2023).
    tweet | link | pdf
17. Lo, A., Pollice, R., Nigam, A., White, A. D., Krenn, M. & Aspuru-Guzik, A. Recent advances in the self-referencing embedded strings (SELFIES) library. Digital Discovery 2, 897–908 (2023).
    link | pdf | code
18. Lála, J., O’Donoghue, O., Shtedritski, A., Cox, S., Rodriques, S. G. & White, A. D. Paperqa: Retrieval-augmented generative agent for scientific research. arXiv preprint arXiv:2312.07559 (2023).
    tweet | link | pdf | code
19. Barrett, R., Ansari, M., Ghoshal, G. & White, A. D. Simulation-based inference with approximately correct parameters via maximum entropy. Machine Learning: Science and Technology 3, 025006 (2022).
    tweet | link | pdf | code
20. Wellawatte, G. P., Seshadri, A. & White, A. D. Model agnostic generation of counterfactual explanations for molecules. Chemical science 13, 3697–3705 (2022).
    tweet | link | pdf | code
21. Ansari, M., Gandhi, H. A., Foster, D. G. & White, A. D. Iterative symbolic regression for learning transport equations. AIChE Journal 68, e17695 (2022).
    tweet | link | pdf | code
22. Hocky, G. M. & White, A. D. Natural language processing models that automate programming will transform chemistry research and teaching. Digital discovery 1, 79–83 (2022).
    tweet | link | pdf | code
23. Ansari, M., Soriano-Paños, D., Ghoshal, G. & White, A. D. Inferring spatial source of disease outbreaks using maximum entropy. Physical Review E 106, 014306 (2022).
    tweet | link | pdf | code
24. Hamsici, S., White, A. D. & Acar, H. Peptide framework for screening the effects of amino acids on assembly. Science Advances 8, eabj0305 (2022).
    tweet | link | pdf | code
25. Krenn, M., Ai, Q., Barthel, S., Carson, N., Frei, A., Frey, N. C., Friederich, P., Gaudin, T., Gayle, A. A., Jablonka, K. M., et al. SELFIES and the future of molecular string representations. Patterns 3, (2022).
    tweet | link | pdf | code
26. Cox, S. & White, A. D. Symmetric molecular dynamics. Journal of Chemical Theory and Computation 18, 4077–4081 (2022).
    tweet | link | pdf | code
27. Kalinin, S. V., Ziatdinov, M., Sumpter, B. G. & White, A. D. Physics is the new data. arXiv preprint arXiv:2204.05095 (2022).
    link | pdf
28. Zhu, W., Luo, J. & White, A. D. Federated learning of molecular properties with graph neural networks in a heterogeneous setting. Patterns 100521 (2022).
    tweet | link | pdf | code
29. White, A. D. Deep learning for molecules and materials. Living journal of computational molecular science 3, (2022).
30. Yang, Z., Milas, K. A. & White, A. D. Now what sequence? Pre-trained ensembles for bayesian optimization of protein sequences. bioRxiv 2022–08 (2022).
    tweet | link | pdf | code
31. Gandhi, H. A. & White, A. D. Explaining molecular properties with natural language. (2022).
    tweet | link | code
32. Seshadri, A., Gandhi, H. A., Wellawatte, G. P. & White, A. D. Why does that molecule smell? (2022).
    tweet | link | pdf | code
33. Yang, Z., Chakraborty, M. & White, A. D. Predicting chemical shifts with graph neural networks. Chemical Science (2021).
    tweet | link | pdf | code
34. Gandhi, H. A. & White, A. D. City-wide modeling of vehicle-to-grid economics to understand effects of battery performance. ACS Sustainable Chemistry & Engineering 9, 14975–14985 (2021).
    tweet | link | pdf | code
35. Chakraborty, M., Xu, J. & White, A. D. Is preservation of symmetry necessary for coarse-graining? Physical Chemistry Chemical Physics 22, 14998–15005 (2020).
    link | pdf
36. Chakraborty, M., Ziatdinov, M., Dyck, O., Jesse, S., White, A. D. & Kalinin, S. V. Reconstruction of the interatomic forces from dynamic scanning transmission electron microscopy data. Journal of Applied Physics 127, (2020).
    link
37. Li, Z., Wellawatte, G. P., Chakraborty, M., Gandhi, H. A., Xu, C. & White, A. D. Graph neural network based coarse-grained mapping prediction. Chemical science 11, 9524–9531 (2020).
    tweet | link | pdf | code
38. Tang, J., Zhang, Y., Luehmann, A. & White, A. Augmented reality improved learning of lower-level students by empowering their participation in collaborative activities. (2020).
    link | pdf
39. Barrett, R., Chakraborty, M., Amirkulova, D., Gandhi, H., Wellawatte, G. & White, A. Hoomd-tf: Gpu-accelerated, online machine learning in the hoomd-blue molecular dynamics engine. Journal of Open Source Software 5, (2020).
    link | pdf | code
40. Amirkulova, D. B., Chakraborty, M. & White, A. D. Experimentally consistent simulation of aβ21–30 peptides with a minimal NMR bias. The Journal of Physical Chemistry B 124, 8266–8277 (2020).
    link | pdf
41. Gandhi, H. A., Jakymiw, S., Barrett, R., Mahaseth, H. & White, A. D. Real-time interactive simulation and visualization of organic molecules. (2020).
    link | code
42. Barrett, R. & White, A. D. Investigating active learning and meta-learning for iterative peptide design. Journal of chemical information and modeling 61, 95–105 (2020).
    link | pdf | code
43. Amirkulova, D. B. & White, A. D. Recent advances in maximum entropy biasing techniques for molecular dynamics. Molecular Simulation 45, 1285–1294 (2019).
    link | pdf
44. Promoting transparency and reproducibility in enhanced molecular simulations. Nature methods 16, 670–673 (2019).
    tweet | link | pdf | code
45. Mayes, H. B., Lee, S., White, A. D., Voth, G. A. & Swanson, J. M. Multiscale kinetic modeling reveals an ensemble of cl–/h+ exchange pathways in ClC-ec1 antiporter. Journal of the American Chemical Society 140, 1793–1804 (2018).
    link | pdf
46. Amirkulova, D. B. & White, A. D. Combining enhanced sampling with experiment-directed simulation of the GYG peptide. Journal of Theoretical and Computational Chemistry 17, 1840007 (2018).
    link | pdf
47. Chakraborty, M., Xu, C. & White, A. D. Encoding and selecting coarse-grain mapping operators with hierarchical graphs. The Journal of Chemical Physics 149, (2018).
    link | pdf | code
48. Barrett, R., Jiang, S. & White, A. D. Classifying antimicrobial and multifunctional peptides with bayesian network models. Peptide Science 110, e24079 (2018).
    link | pdf | code
49. Barrett, R., Gandhi, H. A., Naganathan, A., Daniels, D., Zhang, Y., Onwunaka, C., Luehmann, A. & White, A. D. Social and tactile mixed reality increases student engagement in undergraduate lab activities. Journal of Chemical Education 95, 1755–1762 (2018).
50. White, A. D., Knight, C., Hocky, G. M. & Voth, G. A. Communication: Improved ab initio molecular dynamics by minimally biasing with experimental data. The Journal of Chemical Physics 146, (2017).
51. Freeman, G. M., Drennen, T. E. & White, A. D. Can parked cars and carbon taxes create a profit? The economics of vehicle-to-grid energy storage for peak reduction. Energy Policy 106, 183–190 (2017).
52. Dannenhoffer-Lafage, T., White, A. D. & Voth, G. A. A direct method for incorporating experimental data into multiscale coarse-grained models. Journal of chemical theory and computation 12, 2144–2153 (2016).
53. White, A. D., Dama, J. F. & Voth, G. A. Designing free energy surfaces that match experimental data with metadynamics. Journal of Chemical Theory and Computation 11, 2451–2460 (2015).
54. Shao, Q., White, A. D. & Jiang, S. Difference of carboxybetaine and oligo (ethylene glycol) moieties in altering hydrophobic interactions: A molecular simulation study. The Journal of Physical Chemistry B 118, 189–194 (2014).
55. Nowinski, A. K., White, A. D., Keefe, A. J. & Jiang, S. Biologically inspired stealth peptide-capped gold nanoparticles. Langmuir 30, 1864–1870 (2014).
56. Mi, L., White, A. D., Shao, Q., Setlow, P., Li, Y. & Jiang, S. Chemical insights into dodecylamine spore lethal germination. Chemical Science 5, 3320–3324 (2014).
57. White, A. D. & Voth, G. A. Efficient and minimal method to bias molecular simulations with experimental data. Journal of chemical theory and computation 10, 3023–3030 (2014).
58. White, A. D., Keefe, A. J., Nowinski, A. K., Shao, Q., Caldwell, K. & Jiang, S. Standardizing and simplifying analysis of peptide library data. Journal of chemical information and modeling 53, 493–499 (2013).
59. Brault, N. D., White, A. D., Taylor, A. D., Yu, Q. & Jiang, S. A directly functionalizable surface platform for protein arrays in undiluted human blood plasma. Analytical Chemistry 85, 1447–1453 (2013).
60. Keefe, A. J., Caldwell, K. B., Nowinski, A. K., White, A. D., Thakkar, A. & Jiang, S. Screening nonspecific interactions of peptides without background interference. Biomaterials 34, 1871–1877 (2013).
61. White, A. D., Keefe, A. J., Ella-Menye, J.-R., Nowinski, A. K., Shao, Q., Pfaendtner, J. & Jiang, S. Free energy of solvated salt bridges: A simulation and experimental study. The Journal of Physical Chemistry B 117, 7254–7259 (2013).
62. Nowinski, A. K., Sun, F., White, A. D., Keefe, A. J. & Jiang, S. Sequence, structure, and function of peptide self-assembled monolayers. Journal of the American Chemical Society 134, 6000–6005 (2012).
63. White, A. D., Nowinski, A. K., Huang, W., Keefe, A. J., Sun, F. & Jiang, S. Decoding nonspecific interactions from nature. Chemical Science 3, 3488–3494 (2012).
64. Shao, Q., He, Y., White, A. D. & Jiang, S. Different effects of zwitterion and ethylene glycol on proteins. The Journal of chemical physics 136, (2012).
65. White, A. D., Huang, W. & Jiang, S. Role of nonspecific interactions in molecular chaperones through model-based bioinformatics. Biophysical Journal 103, 2484–2491 (2012).
66. White, A. & Jiang, S. Local and bulk hydration of zwitterionic glycine and its analogues through molecular simulations. The Journal of Physical Chemistry B 115, 660–667 (2011).
67. Shao, Q., He, Y., White, A. D. & Jiang, S. Difference in hydration between carboxybetaine and sulfobetaine. The Journal of Physical Chemistry B 114, 16625–16631 (2010).
68. Yang, W., Zhang, L., Wang, S., White, A. D. & Jiang, S. Functionalizable and ultra stable nanoparticles coated with zwitterionic poly (carboxybetaine) in undiluted blood serum. Biomaterials 30, 5617–5621 (2009).