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Ultra-large-scale docking

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Ultra-large-scale docking, sometimes abbreviated as Ultra-LSD, is an ultra-large-scale approach to protein–ligand docking and virtual screening.[1][2][3][4][5] It employs molecular docking campaigns against libraries of millions or billions of chemical compounds to discover new drugs.[1][3][6][7][8] The virtual screening phase identifies potential high-affinity ligands and then selected promising compounds are synthesized and further evaluated in the laboratory, including in terms of properties like functional activity and selectivity.[1][6] The purpose of Ultra-LSD is to discover novel chemical scaffolds for ligands of molecular targets.[1][3][4] Ultra-LSD was developed by Brian Shoichet and John Irwin at the University of California, San Francisco, Bryan L. Roth at University of North Carolina at Chapel Hill, and other colleagues, and was first described in 2019.[2][4][5]

The researchers have conducted Ultra-LSD campaigns against a variety of targets, including the serotonin 5-HT2A receptor,[1][9][10] the melatonin receptors,[1][11][12][11] the dopamine D4 receptor,[5] and the serotonin 5-HT5A receptor,[13] among others.[14] Some of these studies have notably employed AlphaFold2-generated models of folded receptor structures for molecular docking with ligands.[15][16]

The aim of the serotonin 5-HT2A receptor Ultra-LSD campaign was to identify novel serotonin 5-HT2A receptor agonists, including non-hallucinogenic psychoplastogens for potential medical use as well as serotonergic psychedelics.[17][7][1][6][8] In 2021, it was reported that the serotonin 5-HT2A receptor ULTRA-LSD campaign had computationally screened 11 billion compounds of a library of more than 34 billion compounds.[6][7][8] It was hoped that the project would identify numerous new structural scaffolds of psychedelics.[17][7] The first findings of the campaign were published in 2022.[10] The project led to the identification of novel serotonin 5-HT2A receptor agonists including the non-hallucinogenic Gq-biased agonist (R)-69, the selective serotonin 5-HT2A receptor agonist Z3517967757,[15] and the β-arrestin-biased serotonin 5-HT2A receptor agonist RS130-180, among other compounds.[15][9][18][16] The project received a US$27 million grant from the Defense Advanced Research Projects Agency (DARPA) to develop novel antidepressants.[1][19] The serotonin 5-HT2A receptor campaign was featured by Hamilton Morris in 2021 in the final episode of his TV show Hamilton's Pharmacopeia.[17][7]

Ultra-LSD campaigns generally make use of the ZINC database, a free and publicly available curated library of billions of compounds for virtual screening that was developed by Irwin and Schoichet.[20][21][22][23][24] ZINC was first made available in 2005 and has grown in size exponentially over time, from hundreds of thousands of compounds at launch[20] to billions of compounds in 2022.[24]

References

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  1. ^ a b c d e f g h Langlitz, Nicolas (2024). "Psychedelic innovations and the crisis of psychopharmacology". BioSocieties. 19 (1): 37–58. doi:10.1057/s41292-022-00294-4. ISSN 1745-8552. In the 2010s, computational pharmacologists began to collaborate with software developers on technologies that allow to design new drugs in silico. In 2019, pharmaceutical chemist Brian Shoichet announced that one of the bubbles constraining novel drug discovery had popped (University of California, San Francisco 2019). [...] Instead of screening drug libraries physically, Lyu et al. (2019) had created an ultra-large virtual library of 170 million compounds, which a computer simulation rotated and adjusted to identify those compounds that might bind to a particular receptor or some other target. [...] In 2020, Shoichet's laboratory at the University of California, San Francisco, provided a proof of concept that in silico drug design allowed to discover new drugs. Looking for a medication to treat sleep disorders and jet lag, they searched the virtual library for molecules that specifcally docked to one of the two mammalian melatonin receptors called MT1. They ran computer simulations of 72 trillion drug-receptor interactions and eventually identifed 40 potential drugs. Employing another recently invented technology, the Ukrainian company Enamine was then able to synthesize 38 of these molecules by combining prefabricated chemical building blocks with one another (at a cost of approximately $100 per molecule). At that point, in vitro and in vivo testing allowed Shoichet's group to identify those drugs that actually bound to MT1 and to establish their behavioral efects in mice (Stein et al. 2020). The chemical scafolds of these molecules were unrelated to known melatonin receptor ligands. [...] In light of the promising results of psychedelic-assisted psychotherapy, Brian Roth's laboratory at the University of North Carolina at Chapel Hill received a $27 million grant from the US Department of Defense to use the tools of computational pharmacology to develop drugs that have the therapeutic but not the psychedelic efects of 5-HT2a agonists like LSD and psilocybin. [...] One major reservation regarding Roth's approach to drug discovery was raised in a personal communication with Hamilton Morris: "Roth's ULTRA-LSD* technique is designed to characterize high affinity ligands, which are then further screened for functional activity and receptor selectivity. Neither affinity nor selectivity are in and of themselves a determinant of therapeutic efficacy. [...] concepts like affinity and selectivity, while extremely valuable in pharmacology research, are not immediately applicable to a therapeutic domain especially in the realm of psychedelics."
  2. ^ a b Weiler, Nicholas (6 February 2019). "'Virtual Pharmacology' Advance Tackles Universe of Unknown Drugs". ‘Virtual Pharmacology’ Advance Tackles Universe of Unknown Drugs. Retrieved 6 June 2025. Scientists at UC San Francisco, in collaboration with colleagues at the University of North Carolina (UNC), have developed the world's largest virtual pharmacology platform and shown it is capable of identifying extremely powerful new drugs. The platform, soon to contain over a billion virtual molecules never before synthesized and not found in nature, is poised to dramatically change early drug discovery and send waves through the pharmaceutical industry, the authors say. [...] Now Brian Shoichet, PhD, and John Irwin, PhD — a professor and adjunct associate professor of pharmaceutical chemistry, respectively, in UCSF's School of Pharmacy — have begun to crack this problem through a collaboration with a remarkable chemical supplier based in Ukraine, as described in a study published February 6, 2019 in Nature. [...] Irwin and Shoichet have partnered with Enamine to begin incorporating its vast virtual catalogue into their free public drug discovery database — called ZINC — which currently contains over 750 million compounds and is constantly growing as Enamine and other suppliers add new building-blocks and chemical reactions. [...] "Our platform can now screen 100 times more molecules than are available in most drug screening libraries, with far more diversity in the molecules screened. Soon it will be able to screen 1000 times more," Irwin said. "People are going to have access to a lot of new chemistry that no one has looked at before."
  3. ^ a b c McClure-Begley TD, Roth BL (June 2022). "The promises and perils of psychedelic pharmacology for psychiatry". Nat Rev Drug Discov. 21 (6): 463–473. doi:10.1038/s41573-022-00421-7. PMID 35301459. Discovering new chemical matter with beneficial actions at 5-HT2A receptors will likely be accelerated by ultra-large-scale computational approaches130. In proof-of-concept studies we and others have shown that the ultra-large-scale docking of in silico enumerated molecules can afford the discovery of potent and selective compounds with biased signalling properties at prototypical G protein-coupled receptors (GPCRs)130,131. One can thereby envision a similar strategy aimed at 5-HT2A receptors where, ultimately, billions of compounds might be interrogated computationally at relevant 5-HT2A receptor complexes.
  4. ^ a b c Bryan L. Roth (1 August 2019). VEGAS and ULTRA-L.S.D.: Two New Technologies to Illuminate GPCR Structure and Function (PDF). Chemistry and Pharmacology of Drug Abuse (CPDA) Conference 2019.
  5. ^ a b c Lyu J, Wang S, Balius TE, Singh I, Levit A, Moroz YS, O'Meara MJ, Che T, Algaa E, Tolmachova K, Tolmachev AA, Shoichet BK, Roth BL, Irwin JJ (February 2019). "Ultra-large library docking for discovering new chemotypes". Nature. 566 (7743): 224–229. Bibcode:2019Natur.566..224L. doi:10.1038/s41586-019-0917-9. PMC 6383769. PMID 30728502.
  6. ^ a b c d Apostolides, Marianne (16 December 2021). "The Psychedelic Divide". proto.life. Retrieved 6 June 2025. Twenty years after Roth evolved a human protein through directed evolution, scientists are using the method to create everything from biodegradable plastics and clean energy to new medicines. The technique has gotten exponentially more sophisticated with advances in machine learning. The brute-force approach Roth used to create his first DREADD has been streamlined through AI. Instead of generating compounds in yeast, he can do so through his computer platform, called Ultra LSD. "We're able to access this huge chemical space," Roth says, since the compounds he's evolving "don't exist in the physical universe"—not until he chooses which ones to synthesize. Roth is choosing LSD-like compounds whose binding actions cause the growth of dendrites—the neuronal spines that reach across the brain, connecting to other neurons—but don't cause hallucinations. Through Ultra LSD, Roth sought a drug that could do just that. He examined 11 billion chemical compounds, searching for those that could induce dendritic growth without hallucinations. After choosing a handful of the most promising candidates, he synthesized the drugs and tested them on mice. The results are currently under embargo since publication is pending in a peer-reviewed journal. All Roth can say is that he's "very pleased" with the outcome.
  7. ^ a b c d e Bauer BE (9 February 2021). "Pharmacopeia Season Finale Recap". Psychedelic Science Review. Retrieved 10 April 2025. Bryan Roth is working with what he calls ULTRA-LSD, or Ultra Large Scale Docking. His lab uses in silico methods, among other computer tools, to screen compounds and identify those that are likely drug candidates. Roth is working from a virtual library of over 34 billion small, drug-like molecules, or as he describes it, "This huge universe of chemical scaffolds." From this universe, Roth's goal is to identify 100,000 novel scaffolds for the 5-HT2A receptor. He says, "We can do it. We're doing it." Morris adds, "Should Roth succeed, the product will be countless novel psychedelic scaffolds that may be more potent than the lysergamides." Roth can't reveal what he's learned so far in the project. But what he does say sounds like the beginnings of a whole new world of psychedelic study.
  8. ^ a b c Farah, Troy (26 April 2021). "Scientists are Using AI to Develop New Psychedelic Drugs". DoubleBlind Mag. Roth's lab uses a computational program called Ultra Large Scale Docking (Ultra LSD) that generates millions of permutations of different chemical structures, then predicts how the molecules would fit into the serotonin 2A receptor, which is largely associated with how some psychedelics generate their unique mental effects. LSD, psilocybin and DMT all bind to the serotonin 2A receptor, and Roth has done some of the world's most groundbreaking research on how these drugs, such as LSD, interact at these receptors. "We have basically a three-dimensional model of the [serotonin] receptor [where] there are sites for drugs to bind. The computer takes each drug one by one and puts it in there," Roth explains. If the drug clicks into the serotonin 2A receptor, that's an indication that it will work like a psychedelic. "The library has been expanded considerably and we're planning on docking, I think, 5 billion compounds in the summer." Roth's lab has been slowly picking chemicals that seem like promising new medications and working with a lab that then synthesizes them. Finally, a robot tests the drugs by squirting them at human cells to make sure what the computer predicted matches up with reality. "The project is to make drugs that interact with the same receptor that LSD does, and have the beneficial, putative effects of LSD without having a psychedelic effect," Roth explains. This is accomplished by picking drugs that have slightly different binding profiles than classic psychedelics, but have removed the downstream effects that cause hallucinations and visuals. Already, Roth and his colleagues have narrowed down a few promising new drugs and begun testing them in mice using a battery of tests that can determine if a drug is psychoactive in animals. For people with debilitating conditions like heart problems or schizophrenia, the powerful sway of psychedelics may not be therapeutic—it may even be harmful. A drug that can ease depression as rapidly as a psychedelic without the introspective distortion would be very useful for certain patients.
  9. ^ a b Nichols DE (2018). Chemistry and Structure-Activity Relationships of Psychedelics. Current Topics in Behavioral Neurosciences. Vol. 36. pp. 1–43. doi:10.1007/7854_2017_475. ISBN 978-3-662-55878-2. PMID 28401524. There are now several published examples of novel chemical scaffolds of 5-HT2A receptor agonists being found through large-scale docking studies (Kaplan et al., 2022; Lyu, Kapolka, Gumpper, Alon, Wang, Jain, Barros-Alvarez, Sakamoto, Kim, DiBerto, Kim, & Roth, 2024) or traditional medicinal chemistry efforts (Cameron et al., 2021; Cao, Yu, et al., 2022; Rorsted et al., 2024). For the sake of space, we encourage readers to consult these reviews (Gumpper & Roth, 2024; Kwan et al., 2022; Olson, 2022) for extended commentary on the compounds derived from medicinal chemistry efforts. For example, two compounds have been identified by largescale docking endeavours—(R)-69 and Z7757, shown in Figure 5a,b (Kaplan et al., 2022; Lyu, Kapolka, Gumpper, Alon, Wang, Jain, Barros-Alvarez, Sakamoto, Kim, DiBerto, Kim, & Roth, 2024). (R)-69 arose from a virtual library of tetrahydropyridines (THPs) sampling about 75 million compounds (Kaplan et al., 2022). Although THPs represent a novel class of 5-HT2A receptor agonists, the azaindole substituent of the final compound (R)-69 is chemically similar to the tryptamine class and contains an indole-like moiety. [...] Interestingly, the N-methylation of the THP produced the related compound (R)-70, which lost both potency and efficacy but gained selectivity for 5-HT2A over 5-HT2B receptors (6.4-fold selective). [...] Another compound to come out of a large-scale docking screen is the recently reported Z7757 (Figure 5b,) (Lyu, Kapolka, Gumpper, Alon, Wang, Jain, Barros-Alvarez, Sakamoto, Kim, DiBerto, Kim, & ´ Roth, 2024). This compound was discovered from a 1.6 billion molecule docking screen against the AlphaFold model of the 5-HT2A receptor. Similar to the recent report of LPH-5, Z7757 is a ring-restrained phenethylamine. However, it has a pyrimidine ring substituent coming off the tertiary nitrogen. [...] Remarkably, Z7757 shows excellent selectivity for 5-HT2A receptors with no activity being detected for 5-HT2B or 5-HT2C receptors in calcium mobilization assays, but further optimization to increase potency and in vivo testing is needed.
  10. ^ a b Kaplan AL, Confair DN, Kim K, Barros-Álvarez X, Rodriguiz RM, Yang Y, Kweon OS, Che T, McCorvy JD, Kamber DN, Phelan JP, Martins LC, Pogorelov VM, DiBerto JF, Slocum ST, Huang XP, Kumar JM, Robertson MJ, Panova O, Seven AB, Wetsel AQ, Wetsel WC, Irwin JJ, Skiniotis G, Shoichet BK, Roth BL, Ellman JA (October 2022). "Bespoke library docking for 5-HT2A receptor agonists with antidepressant activity". Nature. 610 (7932): 582–591. doi:10.1038/s41586-022-05258-z. PMC 9996387. PMID 36171289.
  11. ^ a b Patel, Nilkanth; Huang, Xi Ping; Grandner, Jessica M; Johansson, Linda C; Stauch, Benjamin; McCorvy, John D; Liu, Yongfeng; Roth, Bryan; Katritch, Vsevolod (2 March 2020). "Structure-based discovery of potent and selective melatonin receptor agonists". eLife. 9. doi:10.7554/eLife.53779. ISSN 2050-084X. PMC 7080406. PMID 32118583.
  12. ^ Stein RM, Kang HJ, McCorvy JD, Glatfelter GC, Jones AJ, Che T, Slocum S, Huang XP, Savych O, Moroz YS, Stauch B, Johansson LC, Cherezov V, Kenakin T, Irwin JJ, Shoichet BK, Roth BL, Dubocovich ML (March 2020). "Virtual discovery of melatonin receptor ligands to modulate circadian rhythms". Nature. 579 (7800): 609–614. Bibcode:2020Natur.579..609S. doi:10.1038/s41586-020-2027-0. PMC 7134359. PMID 32040955.
  13. ^ Levit Kaplan A, Strachan RT, Braz JM, Craik V, Slocum S, Mangano T, Amabo V, O'Donnell H, Lak P, Basbaum AI, Roth BL, Shoichet BK (March 2022). "Structure-Based Design of a Chemical Probe Set for the 5-HT5A Serotonin Receptor". J Med Chem. 65 (5): 4201–4217. doi:10.1021/acs.jmedchem.1c02031. PMC 9116900. PMID 35195401.
  14. ^ Sadybekov AA, Sadybekov AV, Liu Y, Iliopoulos-Tsoutsouvas C, Huang XP, Pickett J, Houser B, Patel N, Tran NK, Tong F, Zvonok N, Jain MK, Savych O, Radchenko DS, Nikas SP, Petasis NA, Moroz YS, Roth BL, Makriyannis A, Katritch V (January 2022). "Synthon-based ligand discovery in virtual libraries of over 11 billion compounds". Nature. 601 (7893): 452–459. Bibcode:2022Natur.601..452S. doi:10.1038/s41586-021-04220-9. PMC 9763054. PMID 34912117.
  15. ^ a b c Lyu J, Kapolka N, Gumpper R, Alon A, Wang L, Jain MK, Barros-Álvarez X, Sakamoto K, Kim Y, DiBerto J, Kim K, Glenn IS, Tummino TA, Huang S, Irwin JJ, Tarkhanova OO, Moroz Y, Skiniotis G, Kruse AC, Shoichet BK, Roth BL (June 2024). "AlphaFold2 structures guide prospective ligand discovery". Science. 384 (6702): eadn6354. Bibcode:2024Sci...384n6354L. doi:10.1126/science.adn6354. PMC 11253030. PMID 38753765.
  16. ^ a b Callaway E (February 2024). "AlphaFold found thousands of possible psychedelics. Will its predictions help drug discovery?". Nature. 626 (7997): 14–15. Bibcode:2024Natur.626...14C. doi:10.1038/d41586-024-00130-8. PMID 38238624.
  17. ^ a b c Morris H (8 February 2021). "Ultra LSD". Hamilton's Pharmacopeia. Season 3. Episode 6. Vice Media. Event occurs at [...] Viceland. [...]
  18. ^ Gumpper RH, Jain MK, Kim K, Sun R, Sun N, Xu Z, DiBerto JF, Krumm BE, Kapolka NJ, Kaniskan HÜ, Nichols DE, Jin J, Fay JF, Roth BL (March 2025). "The structural diversity of psychedelic drug actions revealed". Nature Communications. 16 (1): 2734. Bibcode:2025NatCo..16.2734G. doi:10.1038/s41467-025-57956-7. PMC 11923220. PMID 40108183. We next examined the binding mode of the βarrestin-2 biased N-benzylated phenethylamine, RS130-180 (see Supplementary Fig. 10 for NMR validation and Supplementary Fig. 11 for the synthetic scheme), which has shown utility as an in vitro tool compound, although it has suboptimal in vivo pharmacokinetic properties. RS130-180 was optimized for potency and bias from ZINC000341335936 ('5936), which was identified from a large-scale docking campaign24.
  19. ^ Eanes, Zachery (25 June 2021). "Why a UNC professor is on a quest to remove the 'trip' from psychedelic drugs". Raleigh News & Observer. Retrieved 6 June 2025. That idea prompted a $27 million grant from the Defense Advanced Research Projects Agency (DARPA), a secretive research organization within the U.S. military. DARPA funds what it calls "high-risk, high-reward" research — the kind that will likely fail but could provide outsized benefits. [...] The initial research is happening on computers that can crunch enormous amounts of calculations. The computer program, called Ultra Large-Scale-Docking, is able to generate a billion theoretical psychedelic compounds, all of which score differently in how they activate the 5-HT-2A receptor. [...] The compounds don't exist in the physical world yet. But Roth's team plans to make the ones the computer identifies as being the likeliest to activate the serotonin receptor without triggering hallucinations. [...] "We may fail," Roth acknowledges, noting it might be impossible to separate the therapy from the trip. But that's all part of the game for DARPA. It could be transformational medicine, or Roth and his team may just have created new psychedelics.
  20. ^ a b Irwin JJ, Shoichet BK (2005). "ZINC--a free database of commercially available compounds for virtual screening". J Chem Inf Model. 45 (1): 177–182. doi:10.1021/ci049714+. PMC 1360656. PMID 15667143.
  21. ^ Irwin JJ, Sterling T, Mysinger MM, Bolstad ES, Coleman RG (July 2012). "ZINC: a free tool to discover chemistry for biology". J Chem Inf Model. 52 (7): 1757–1768. doi:10.1021/ci3001277. PMC 3402020. PMID 22587354.
  22. ^ Sterling T, Irwin JJ (November 2015). "ZINC 15--Ligand Discovery for Everyone". J Chem Inf Model. 55 (11): 2324–37. doi:10.1021/acs.jcim.5b00559. PMC 4658288. PMID 26479676.
  23. ^ Irwin JJ, Tang KG, Young J, Dandarchuluun C, Wong BR, Khurelbaatar M, Moroz YS, Mayfield J, Sayle RA (December 2020). "ZINC20-A Free Ultralarge-Scale Chemical Database for Ligand Discovery". J Chem Inf Model. 60 (12): 6065–6073. doi:10.1021/acs.jcim.0c00675. PMC 8284596. PMID 33118813.
  24. ^ a b Tingle BI, Tang KG, Castanon M, Gutierrez JJ, Khurelbaatar M, Dandarchuluun C, Moroz YS, Irwin JJ (February 2023). "ZINC-22─A Free Multi-Billion-Scale Database of Tangible Compounds for Ligand Discovery". J Chem Inf Model. 63 (4): 1166–1176. doi:10.1021/acs.jcim.2c01253. PMC 9976280. PMID 36790087.
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