CoChat
Sign Up
PreprintCited by 102Computational Drug Discovery MethodsProtein Degradation and InhibitorsChemical Synthesis and Analysis

The DOI matches its Crossref record; 63 of the 64 references it cites were found in a registry or an index, and 1 are not checked yet.

Link-INVENT: Generative Linker Design with Reinforcement Learning

Open access
Guo, Jeff, Knuth, Franziska, Margreitter, Christian, Janet, Jon Paul, Papadopoulos, Kostas, Engkvist, Ola, Patronov, Atanas·2022·Digital Discovery
DOI 10.26434/chemrxiv-2022-qkx9f
Open this paper in CoChat — free accountOpen PDF

Abstract

In this work, we present Link-INVENT as an extension to the existing de novo molecular design platform REINVENT. We provide illustrative examples on how Link-INVENT can be applied on fragment linking, scaffold hopping, and PROTACs design case studies where the desirable molecules should satisfy a combination of different criteria. With the help of Reinforcement Learning, the agent used by Link-INVENT learns to generate favourable linkers connecting molecular subunits that satisfy diverse objectives, facilitating practical application of the model for real-world drug discovery projects. We also introduce a range of linker-specific objectives in the scoring function of REINVENT. The code is freely available at https://github.com/MolecularAI/Reinvent.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.26434/chemrxiv-2022-qkx9f

Cites

63 of 64 references verified0 citation errors · 0 not checkable

Statistics

Figures

Fig. 1 Link-INVENT training and inference overview. ChEMBL data were processed to generate the training data for Link-INVENT which features an encoder-decoder architecture of recurrent neural networks (RNNs). Link-INVENT takes as input a pair of warheads and generates output linkers (highlighted), yielding connected molecules. One crucial capability of Link-INVENT is the fl exible scoring.
Fig. 1 Link-INVENT training and inference overview. ChEMBL data were processed to generate the training data for Link-INVENT which features an encoder-decoder architecture of recurrent neural networks (RNNs). Link-INVENT takes as input a pair of warheads and generates output linkers (highlighted), yielding connected molecules. One crucial capability of Link-INVENT is the fl exible scoring.CC-BY · From the PDF
Fig. 2 Link-INVENT Scoring Function selected linker speci fi c properties. Attachment points are denoted by R-groups. (a) ' Linker e ff ective length ' , ' linker maximum graph length ' , and ' linker length ratio ' provide direct control over the distance between attachment atoms in a linker and the degree of branching permitted. The maximum graph length bond traversal is highlighted in the bottom example. (b) ' Linker ratio of rotatable bonds ' provides control over the fl exibility of proposed linkers.
Fig. 2 Link-INVENT Scoring Function selected linker speci fi c properties. Attachment points are denoted by R-groups. (a) ' Linker e ff ective length ' , ' linker maximum graph length ' , and ' linker length ratio ' provide direct control over the distance between attachment atoms in a linker and the degree of branching permitted. The maximum graph length bond traversal is highlighted in the bottom example. (b) ' Linker ratio of rotatable bonds ' provides control over the fl exibility of proposed linkers.CC-BY · From the PDF
Image 3
CC-BY · From the PDF
Fig. 4 Experiment 1a: fragment linking strategy for casein kinase 2 inhibitors for the alpha catalytic site (CK2 a ). (a) Initial fragment hits. The fragment structures are colour-coded: gray fragment PDB ID: 5CSV and green fragment PDB ID: 5CSH. The gray fragment binds by forming hydrogen-bond interactions with Lys68 and Asp175 while the green fragment binds via hydrophobic interactions. The fragment linking strategy was to leverage the nitrogen atoms on both fragments to design a linear linker, separated by 9.9 Å. (b) Fragment linking led to the discovery of the linked molecule, CAM4066 (PDB ID: 5CU4). The constituent fragments are circled in the structure. The linear linker features amide bonds that modulate the linker fl exibility and rigidity which the authors attribute to its binding potency. 52,53
Fig. 4 Experiment 1a: fragment linking strategy for casein kinase 2 inhibitors for the alpha catalytic site (CK2 a ). (a) Initial fragment hits. The fragment structures are colour-coded: gray fragment PDB ID: 5CSV and green fragment PDB ID: 5CSH. The gray fragment binds by forming hydrogen-bond interactions with Lys68 and Asp175 while the green fragment binds via hydrophobic interactions. The fragment linking strategy was to leverage the nitrogen atoms on both fragments to design a linear linker, separated by 9.9 Å. (b) Fragment linking led to the discovery of the linked molecule, CAM4066 (PDB ID: 5CU4). The constituent fragments are circled in the structure. The linear linker features amide bonds that modulate the linker fl exibility and rigidity which the authors attribute to its binding potency. 52,53CC-BY · From the PDF
Fig. 5 Experiment 1a: fragment linking Link-INVENT results. The experiment was run in triplicate. The curve shows the average score achieved by the batch of molecules sampled at a given epoch and the upper and lower bounds of the shaded region represent the maximum and minimum scores, respectively. (a) Glide LigPrep docking score optimization. The average docking score achieved by the batch of compounds generated by Link-INVENT gradually becomes more favourable (lower score in the case of Glide). (b) Violin plots showing the distribution of docking scores for the triplicate runs. ' N ' is the number of molecules generated over 100 epochs. The distributions are nearly identical, demonstrating a reproducible experimental outcome. The black dotted line shows the docking score possessed by the reference ligand (CAM4066, -15.20 kcal mol -1 ). (c) Venn diagram plots showing the overlap between unique Bemis -Murcko sca ff olds in the triplicate runs. (d) The binding pose of a selected generated molecule (green) superimposed with the reference ligand (gray) and the constituent fragments are circled. PDB ID: 5CU4. The structure of the generated molecule is similar to the reference ligand. The yellow and turquoise dotted lines show the interactions formed by the reference ligand and generated ligand, respectively. The generated molecule retains the Lys68 interaction as enforced by the docking constraint and forms the same polar interactions as the reference ligand, largely attributed to the extensive overlap between the binding poses and supporting plausibility.
Fig. 5 Experiment 1a: fragment linking Link-INVENT results. The experiment was run in triplicate. The curve shows the average score achieved by the batch of molecules sampled at a given epoch and the upper and lower bounds of the shaded region represent the maximum and minimum scores, respectively. (a) Glide LigPrep docking score optimization. The average docking score achieved by the batch of compounds generated by Link-INVENT gradually becomes more favourable (lower score in the case of Glide). (b) Violin plots showing the distribution of docking scores for the triplicate runs. ' N ' is the number of molecules generated over 100 epochs. The distributions are nearly identical, demonstrating a reproducible experimental outcome. The black dotted line shows the docking score possessed by the reference ligand (CAM4066, -15.20 kcal mol -1 ). (c) Venn diagram plots showing the overlap between unique Bemis -Murcko sca ff olds in the triplicate runs. (d) The binding pose of a selected generated molecule (green) superimposed with the reference ligand (gray) and the constituent fragments are circled. PDB ID: 5CU4. The structure of the generated molecule is similar to the reference ligand. The yellow and turquoise dotted lines show the interactions formed by the reference ligand and generated ligand, respectively. The generated molecule retains the Lys68 interaction as enforced by the docking constraint and forms the same polar interactions as the reference ligand, largely attributed to the extensive overlap between the binding poses and supporting plausibility.CC-BY · From the PDF
Fig. 6 Experiment 1b: comparison fragment linking inosine 5 ′ -monophosphate dehydrogenase (IMPDH) inhibitors for tuberculosis (TB). (a) Initial fragment hits (PDB ID: 5OU2). Trapero et al. linked copies of the fragment hit separated by 4.6 Å via a linear linker. 61 (b) Fragment linking led to the discovery of the linked molecule shown in gray (PDB ID: 5OU3) and possessing signi fi cantly enhanced in vitro potency. The constituent fragments are circled in the structure. The methyl substituent on the imidazole ring of the right fragment in (b) is not present in the initial fragment hit structure and was added post linker design. 61 A Link-INVENT generated molecule is superimposed with the reference ligand (green), showing excellent pose overlap of the constituent fragments and with a comparable docking score.
Fig. 6 Experiment 1b: comparison fragment linking inosine 5 ′ -monophosphate dehydrogenase (IMPDH) inhibitors for tuberculosis (TB). (a) Initial fragment hits (PDB ID: 5OU2). Trapero et al. linked copies of the fragment hit separated by 4.6 Å via a linear linker. 61 (b) Fragment linking led to the discovery of the linked molecule shown in gray (PDB ID: 5OU3) and possessing signi fi cantly enhanced in vitro potency. The constituent fragments are circled in the structure. The methyl substituent on the imidazole ring of the right fragment in (b) is not present in the initial fragment hit structure and was added post linker design. 61 A Link-INVENT generated molecule is superimposed with the reference ligand (green), showing excellent pose overlap of the constituent fragments and with a comparable docking score.CC-BY · From the PDF
Fig. 7 Experiment 2: sca ff old hopping strategy for dual leucine zipper kinase (DLK) inhibitor optimization. (a) Initial inhibitor possessing poor physico-chemical properties causing in vivo high clearance (PDB ID: 5CEO). The two hydrogen bonds in the hinge region with Cys193 are crucial for potency. The goal was to replace the pyridine core while retaining the Cys193 interactions. (b) Sca ff old hopping led to the discovery of a DLK inhibitor with a pyrazole core and with demonstrated in vivo e ffi cacy (PDB ID: 5CEQ). 64,65 The retained molecular sub-units are circled in the structure.
Fig. 7 Experiment 2: sca ff old hopping strategy for dual leucine zipper kinase (DLK) inhibitor optimization. (a) Initial inhibitor possessing poor physico-chemical properties causing in vivo high clearance (PDB ID: 5CEO). The two hydrogen bonds in the hinge region with Cys193 are crucial for potency. The goal was to replace the pyridine core while retaining the Cys193 interactions. (b) Sca ff old hopping led to the discovery of a DLK inhibitor with a pyrazole core and with demonstrated in vivo e ffi cacy (PDB ID: 5CEQ). 64,65 The retained molecular sub-units are circled in the structure.CC-BY · From the PDF
Fig. 8 Experiment 2: sca ff old hopping Link-INVENT results. The experiment was run in triplicate. The curve shows the average score achieved by the batch of molecules sampled at a given epoch and the upper and lower bounds of the shaded region represent the maximum and minimum scores, respectively. The analysis is identical to the fragment linking experiments. (a) Glide LigPrep docking score optimization. The average docking score achieved by the batch of compounds generated by Link-INVENT. (b) Violin plots showing the distribution of docking scores for the triplicate runs. ' N ' is the number of molecules generated over 100 epochs that satisfy all the CNS criteria. The black dotted line shows the docking score possessed by the reference ligand ( -11.26 kcal mol -1 ). (c) Venn diagram plots showing the overlap between unique Bemis -Murcko sca ff olds in the triplicate runs. (d) The binding pose of a selected generated molecule (green) superimposed with the reference ligand (gray) and the retained molecular sub-units are circled. PDB ID: 5CEO. The yellow and turquoise dotted lines show the interactions formed by the reference ligand and generated ligand, respectively. The generated molecule retains the Cys193 interaction as enforced by the docking constraint and is predicted to form an additional hydrogen-bond interaction with Gln195. The extensive overlap between the binding poses of the generated ligand and the reference ligand supports plausibility.
Fig. 8 Experiment 2: sca ff old hopping Link-INVENT results. The experiment was run in triplicate. The curve shows the average score achieved by the batch of molecules sampled at a given epoch and the upper and lower bounds of the shaded region represent the maximum and minimum scores, respectively. The analysis is identical to the fragment linking experiments. (a) Glide LigPrep docking score optimization. The average docking score achieved by the batch of compounds generated by Link-INVENT. (b) Violin plots showing the distribution of docking scores for the triplicate runs. ' N ' is the number of molecules generated over 100 epochs that satisfy all the CNS criteria. The black dotted line shows the docking score possessed by the reference ligand ( -11.26 kcal mol -1 ). (c) Venn diagram plots showing the overlap between unique Bemis -Murcko sca ff olds in the triplicate runs. (d) The binding pose of a selected generated molecule (green) superimposed with the reference ligand (gray) and the retained molecular sub-units are circled. PDB ID: 5CEO. The yellow and turquoise dotted lines show the interactions formed by the reference ligand and generated ligand, respectively. The generated molecule retains the Cys193 interaction as enforced by the docking constraint and is predicted to form an additional hydrogen-bond interaction with Gln195. The extensive overlap between the binding poses of the generated ligand and the reference ligand supports plausibility.CC-BY · From the PDF
Fig. 9 Experiment 3: PROTAC strategy for Bcl-2 and Mcl-1 dual degradation. (a) Molecular dynamics (MD) simulated ternary complex of one of the developed PROTACs binding to Mcl-1 (orange) and cereblon (CRBN, blue). The ternary complex is stabilized by protein -protein interactions (PPIs). The linker is an alkyl chain and is circled in the structure. Mcl-1 PDB ID: 2PQK and CRBN PDB ID: 4TZ4. (b) The naphthalimide-based m M inhibitor which was linked with pomalidomide to form the ternary complex. The R-groups denote the linker attachment points. The two linkers that achieved potent and selective in vitro degradation are shown.
Fig. 9 Experiment 3: PROTAC strategy for Bcl-2 and Mcl-1 dual degradation. (a) Molecular dynamics (MD) simulated ternary complex of one of the developed PROTACs binding to Mcl-1 (orange) and cereblon (CRBN, blue). The ternary complex is stabilized by protein -protein interactions (PPIs). The linker is an alkyl chain and is circled in the structure. Mcl-1 PDB ID: 2PQK and CRBN PDB ID: 4TZ4. (b) The naphthalimide-based m M inhibitor which was linked with pomalidomide to form the ternary complex. The R-groups denote the linker attachment points. The two linkers that achieved potent and selective in vitro degradation are shown.CC-BY · From the PDF
Fig. 10 Experiment 3: PROTAC Link-INVENT results. The experiments were run in triplicate. The curve in (c) shows the average score achieved by the batch of molecules sampled at a given epoch and the upper and lower bounds of the shaded region represent the maximum and minimum scores, respectively. (a) Experiment that fi xes physico-chemical properties and tasks Link-INVENT with generating linkers with an e ff ective length within the speci fi ed intervals: [4, 6], [7, 9], [10, 12], and [13, 15]. The baseline experiment does not enforce the linker length. Consequently, the e ff ective linker lengths resemble a broad distribution. The generated linkers from the triplicate runs which also satisfy all the physico-chemical property criteria are aggregated in the plot. (b) Experiment that fi xes physico-chemical properties and the e ff ective linker length within the interval [7 -9]. Link-INVENT is tasked with generating linear and cyclic linkers. The baseline experiment does not enforce linker linearity and approximately a 1 : 2 ratio of linear : cyclic is observed. The generated linkers from the triplicate runs which also satisfy all the physico-chemical property criteria are aggregated in the plot. (c) Experiment that tasks Link-INVENT with generating linkers containing a ratio of rotatable bonds within the speci fi ed intervals: ' Low ' [0, 30], ' Moderate ' [40, 60], and ' High ' [70, 100]. The agent implicitly learns that linkers containing rings and sp 2 hybridized atoms achieve a low ratio of rotatable bonds. Conversely, linear linkers with sp 3 hybridized atoms achieve a high ratio of rotatable bonds.
Fig. 10 Experiment 3: PROTAC Link-INVENT results. The experiments were run in triplicate. The curve in (c) shows the average score achieved by the batch of molecules sampled at a given epoch and the upper and lower bounds of the shaded region represent the maximum and minimum scores, respectively. (a) Experiment that fi xes physico-chemical properties and tasks Link-INVENT with generating linkers with an e ff ective length within the speci fi ed intervals: [4, 6], [7, 9], [10, 12], and [13, 15]. The baseline experiment does not enforce the linker length. Consequently, the e ff ective linker lengths resemble a broad distribution. The generated linkers from the triplicate runs which also satisfy all the physico-chemical property criteria are aggregated in the plot. (b) Experiment that fi xes physico-chemical properties and the e ff ective linker length within the interval [7 -9]. Link-INVENT is tasked with generating linear and cyclic linkers. The baseline experiment does not enforce linker linearity and approximately a 1 : 2 ratio of linear : cyclic is observed. The generated linkers from the triplicate runs which also satisfy all the physico-chemical property criteria are aggregated in the plot. (c) Experiment that tasks Link-INVENT with generating linkers containing a ratio of rotatable bonds within the speci fi ed intervals: ' Low ' [0, 30], ' Moderate ' [40, 60], and ' High ' [70, 100]. The agent implicitly learns that linkers containing rings and sp 2 hybridized atoms achieve a low ratio of rotatable bonds. Conversely, linear linkers with sp 3 hybridized atoms achieve a high ratio of rotatable bonds.CC-BY · From the PDF

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 62 references

Research works printed but not linked: 56

  1. P. Schneider, W. P. Walters, A. T. Plowright, N. Sieroka, J. Listgarten, R. A. Goodnow, J. Fisher, J. M. Jansen, J. S. Duca, T. S. Rush, M. Zentgraf, J. E. Hill, E. Krutoholow, M. Kohler, J. Blaney, K. Funatsu, C. Luebkemann and G. Schneider, Nat. Rev. Drug Discovery, 2020, 19, 353-364.
  2. P. G. Polishchuk, T. I. Madzhidov and A. Varnek, J. Comput.- Aided Mol. Des., 2013, 27, 675-679.
  3. T. Blaschke, J. Arús-Pous, H. Chen, C. Margreitter, C. Tyrchan, O. Engkvist, K. Papadopoulos and A. Patronov, J. Chem. Inf. Model., 2020, 60, 5918-5922.
  4. M. Thomas, R. T. Smith, N. M. O'Boyle, C. de Graaf and A. Bender, J. Cheminf., 2021, 13, 39.
  5. Y. Li, L. Zhang and Z. Liu, J. Cheminf., 2018, 10, 33.
  6. R. Mercado, T. Rastemo, E. Lindelöf, G. Klambauer, O. Engkvist, H. Chen and E. Jannik Bjerrum, Mach. Learn.: Sci. Technol., 2021, 2, 025023.
  7. S. R. Atance, J. V. Diez, O. Engkvist, S. Olsson and R. Mercado, J. Chem. Inf. Model., 2022, 62(20), 4863-4872.
  8. R. Gómez-Bombarelli, J. N. Wei, D. Duvenaud, J. M. Hernández-Lobato, B. Sánchez-Lengeling, D. Sheberla, J. Aguilera-Iparraguirre, T. D. Hirzel, R. P. Adams and A. Aspuru-Guzik, ACS Cent. Sci., 2018, 4, 268-276.
  9. J. Choi and J. Lee, Int. J. Mol. Sci., 2021, 22(21), 11635.
  10. A. Nigam, R. Pollice and A. Aspuru-Guzik, Digital Discovery, 2022, 1(4), 390-404.
  11. D. Weininger, J. Chem. Inf. Comput. Sci., 1988, 28, 31-36.
  12. F. Imrie, A. R. Bradley, M. van der Schaar and C. M. Deane, J. Chem. Inf. Model., 2020, 60, 1983-1995.
  13. Y. Yang, S. Zheng, S. Su, C. Zhao, J. Xu and H. Chen, Chem. Sci., 2020, 11, 8312-8322.
  14. L. Hu, Y. Yang, S. Zheng, J. Xu, T. Ran and H. Chen, J. Chem. Inf. Model., 2021, 61, 4900-4912.
  15. M. Langevin, H. Minoux, M. Levesque and M. Bianciotto, J. Chem. Inf. Model., 2020, 60, 5637-5646.
  16. F. Imrie, T. E. Hadeld, A. R. Bradley and C. M. Deane, Chem. Sci., 2021, 12, 14577-14589.
  17. Y. Feng, Y. Yang, W. Deng, H. Chen and T. Ran, Artif. Intell. Life Sci., 2022, 2, 100035.
  18. D. A. Erlanson, R. S. McDowell and T. O'Brien, J. Med. Chem., 2004, 47, 3463-3482.
  19. C. W. Murray and D. C. Rees, Nat. Chem., 2009, 1, 187-192.
  20. H.-J. Böhm, A. Flohr and M. Stahl, Drug Discovery Today: Technol., 2004, 1, 217-224.
  21. K. M. Sakamoto, K. B. Kim, A. Kumagai, F. Mercurio, C. M. Crews and R. J. Deshaies, Proc. Natl. Acad. Sci. U. S. A., 2001, 98, 8554-8559.
  22. M. Toure and C. M. Crews, Angew. Chem., Int. Ed., 2016, 55, 1966-1973.
  23. X. Sun, H. Gao, Y. Yang, M. He, Y. Wu, Y. Song, Y. Tong and Y. Rao, Signal Transduction Targeted Ther., 2019, 4, 1-33.
  24. J. D. Chodera and D. L. Mobley, Annu. Rev. Biophys., 2013, 42, 121-142.
  25. A. Bancet, C. Raingeval, T. Lomberget, M. Le Borgne, J.-F. Guichou and I. Krimm, J. Med. Chem., 2020, 63, 11420-11435.
  26. H. S. Yu, K. Modugula, O. Ichihara, K. Kramschuster, S. Keng, R. Abel and L. Wang, J. Chem. Theory Comput., 2021, 17, 450-462.
  27. C. A. Lipinski, F. Lombardo, B. W. Dominy and P. J. Feeney, Adv. Drug Delivery Rev., 1997, 23, 3-25.
  28. H. J. Maple, N. Clayden, A. Baron, C. Stacey and R. Felix, Med. Chem. Commun., 2019, 10, 1755-1764.
  29. G. Weng, C. Shen, D. Cao, J. Gao, X. Dong, Q. He, B. Yang, D. Li, J. Wu and T. Hou, Nucleic Acids Res., 2021, 49, D1381-D1387.
  30. T. A. Bemis, J. J. La Clair and M. D. Burkart, J. Med. Chem., 2021, 64, 8042-8052.
  31. T. Cheeseright, M. Mackey, S. Rose and A. Vinter, J. Chem. Inf. Model., 2006, 46, 665-676.
  32. P. Maass, T. Schulz-Gasch, M. Stahl and M. Rarey, J. Chem. Inf. Model., 2007, 47, 390-399.
  33. D. C. Thompson, R. A. Denny, R. Nilakantan, C. Humblet, D. Joseph-McCarthy and E. Feyfant, J. Comput.-Aided Mol. Des., 2008, 22, 761-772.
  34. M. J. Vainio, T. Kogej, F. Raubacher and J. Sadowski, J. Chem. Inf. Model., 2013, 53, 1825-1835.
  35. A. Yoshimori, Y. Asawa, E. Kawasaki, T. Tasaka, S. Matsuda, T. Sekikawa, S. Tanabe, M. Neya, H. Natsugari and C. Kanai, ChemMedChem, 2021, 16, 955-958.
  36. A. Gaulton, A. Hersey, M. Nowotka, A. P. Bento, J. Chambers, D. Mendez, P. Mutowo, F. Atkinson, L. J. Bellis, E. Cibrián- Uhalte, M. Davies, N. Dedman, A. Karlsson, M. P. Magariños, J. P. Overington, G. Papadatos, I. Smit and A. R. Leach, Nucleic Acids Res., 2017, 45, D945-D954.
  37. V. Fialková, J. Zhao, K. Papadopoulos, O. Engkvist, E. Bjerrum, T. Kogej and A. Patronov, J. Chem. Inf. Model., 2022, 62(9), 2046-2063.
  38. J. Arús-Pous, A. Patronov, E. J. Bjerrum, C. Tyrchan, J.-L. Reymond, H. Chen and O. Engkvist, J. Cheminf., 2020, 12, 38.
  39. S. Hochreiter and J. Schmidhuber, Neural Comput., 1997, 9, 1735-1780.
  40. G. W. Bemis and M. A. Murcko, J. Med. Chem., 1996, 39, 2887-2893.
  41. J. Arús-Pous, S. V. Johansson, O. Prykhodko, E. J. Bjerrum, C. Tyrchan, J.-L. Reymond, H. Chen and O. Engkvist, J. Cheminf., 2019, 11, 71.
  42. R. J. Williams and D. Zipser, Neural Comput., 1989, 1, 270- 280.
  43. M. Olivecrona, T. Blaschke, O. Engkvist and H. Chen, J. Cheminf., 2017, 9, 48.
  44. T. Blaschke, O. Engkvist, J. Bajorath and H. Chen, J. Cheminf., 2020, 12, 68.
  45. C. De Fusco, P. Brear, J. Iegre, K. H. Georgiou, H. F. Sore, M. Hyvönen and D. R. Spring, Bioorg. Med. Chem., 2017, 25, 3471-3482.
  46. P. Brear, C. De Fusco, K. Hadje Georgiou, N. J. Francis- Newton, C. J. Stubbs, H. F. Sore, A. R. Venkitaraman, C. Abell, D. R. Spring and M. Hyvönen, Chem. Sci., 2016, 7, 6839-6845.
  47. J. Guo, J. P. Janet, M. R. Bauer, E. Nittinger, K. A. Giblin, K. Papadopoulos, A. Voronov, A. Patronov, O. Engkvist and C. Margreitter, J. Cheminf., 2021, 13, 89. 55 Schrödinger Release 2019-4: Glide, Schrödinger, LLC, New York, NY, 2019.
  48. R. A. Friesner, J. L. Banks, R. B. Murphy, T. A. Halgren, J. J. Klicic, D. T. Mainz, M. P. Repasky, E. H. Knoll, M. Shelley, J. K. Perry, D. E. Shaw, P. Francis and P. S. Shenkin, J. Med. Chem., 2004, 47, 1739-1749.
  49. T. A. Halgren, R. B. Murphy, R. A. Friesner, H. S. Beard, L. L. Frye, W. T. Pollard and J. L. Banks, J. Med. Chem., 2004, 47, 1750-1759.
  50. R. A. Friesner, R. B. Murphy, M. P. Repasky, L. L. Frye, J. R. Greenwood, T. A. Halgren, P. C. Sanschagrin and D. T. Mainz, J. Med. Chem., 2006, 49, 6177-6196. 59 Schrödinger Release 2019-4: LigPrep, Schrödinger, LLC, New York, NY, 2019.
  51. A. Alex, D. S. Millan, M. Perez, F. Wakenhut and G. A. Whitlock, Med. Chem. Commun., 2011, 2, 669-674.
  52. A. Trapero, A. Pacitto, V. Singh, M. Sabbah, A. G. Coyne, V. Mizrahi, T. L. Blundell, D. B. Ascher and C. Abell, J. Med. Chem., 2018, 61, 2806-2822.
  53. O. Trott and A. J. Olson, J. Comput. Chem., 2010, 31, 455-461.
  54. S. Patel, F. Cohen, B. J. Dean, K. De La Torre, G. Deshmukh, A. A. Estrada, A. S. Ghosh, P. Gibbons, A. Gustafson, M. P. Huestis, C. E. Le Pichon, H. Lin, W. Liu, X. Liu, Y. Liu, C. Q. Ly, J. P. Lyssikatos, C. Ma, K. Scearce-Levie, Y. G. Shin, H. Solanoy, K. L. Stark, J. Wang, B. Wang, X. Zhao, J. W. Lewcock and M. Siu, J. Med. Chem., 2015, 58, 401-418.
  55. S. Patel, S. F. Harris, P. Gibbons, G. Deshmukh, A. Gustafson, T. Kellar, H. Lin, X. Liu, Y. Liu, Y. Liu, C. Ma, K. Scearce-Levie, A. S. Ghosh, Y. G. Shin, H. Solanoy, J. Wang, B. Wang, J. Yin, M. Siu and J. W. Lewcock, J. Med. Chem., 2015, 58, 8182-8199.
  56. Z. Wang, N. He, Z. Guo, C. Niu, T. Song, Y. Guo, K. Cao, A. Wang, J. Zhu, X. Zhang and Z. Zhang, J. Med. Chem., 2019, 62, 8152-8163.

Cited by

Sorted by citations

Related

See all

Metadata

Authors
Guo, J., Knuth, F., Margreitter, C., Janet, J. P., Papadopoulos, K., Engkvist, O., & Patronov, A.
Affiliations
  • AstraZeneca (Sweden) (SE)
  • Norwegian University of Science and Technology (NO)
Corresponding author
Patronov, Atanas
ORCID
Venue
Digital Discovery
Publisher
American Chemical Society (ACS)
Published
2022-04-24
Volume
2
Issue
2
Pages
392-408
ISSN
2635-098X (ISSN-L)
DOI
10.26434/chemrxiv-2022-qkx9f
OpenAlex
W4319164354
Cited by
101
References
62
Citations by year
2026: 30 · 2025: 29 · 2024: 26 · 2023: 15 · 2022: 1
FWCI
14.46
Type
journal article
Language
en
Open access
Licence
Locations
  • Digital Discovery (publishedVersion, cc-by)
  • ChemRxiv (acceptedVersion, cc-by)
  • Chalmers Research (Chalmers University of Technology) (submittedVersion)
Topics
  • Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Degradation and Inhibitors
  • Physical Sciences › Chemistry › Organic Chemistry › Chemical Synthesis and Analysis
Keywords
PROTACs, linker design, fragment linking, scaffold hopping, generative design, reinforcement learning

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review