Publications
Here are my publications, in chronological order and by affiliation.
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All
- Practically significant method comparison protocols for machine learning in small molecule drug discovery.
J.R. Ash, C. Wognum, R. Rodríguez-Pérez, M. Aldeghi, A.C, Cheng, D. Clevert, O. Engkvist, C. Fang, D.J. Price, J.M. Hughes-Oliver, W.P. Walters
ChemRxiv2024 - Prospective evaluation of structure-based simulations reveal their ability to predict the impact of kinase mutations on inhibitor binding.
S. Singh, V. Gapsys, M. Aldeghi, D. Schaller, A.M. Rangwala, J.B. White, J.P. Bluck, J. Scheen, W.G. Glass, J. Guo, S. Hayat, B.L. de Groot, A. Volkamer, C.D. Christ, M.A. Seeliger, J.D. Chodera
bioRxiv2024 - A call for an industry-led initiative to critically assess machine learning for real-world drug discovery.
C. Wognum, J.R. Ash, M. Aldeghi, R. Rodríguez-Pérez, C. Fang, A.C. Cheng, D.J. Price, D. Clevert, O. Engkvist, W.P. Walters
Nature Machine Intelligence2024 , 6, 1120−1121 - Anubis: Bayesian optimization with unknown feasibility constraints for scientific experimentation.
R. Hickman, M. Aldeghi, A. Aspuru-Guzik
ChemRxiv2023 - Atlas: A Brain for Self-driving Laboratories.
R. Hickman, M. Sim, S. Pablo-García, I. Woolhouse, H. Hao, Z. Bao, P. Bannigan, C. Allen, M. Aldeghi, A. Aspuru-Guzik
ChemRxiv2023 - Olympus, enhanced: benchmarking mixed-parameter and multi-objective optimization in chemistry and materials science.
R. Hickman, P. Parakh, A. Cheng, Q. Ai, J. Schrier, M. Aldeghi, A. Aspuru-Guzik
ChemRxiv2023 - Machine learning models to accelerate the design of polymeric long-acting injectables.
P. Bannigan, Z. Bao, R.J. Hickman, M. Aldeghi, F. Häse, A. Aspuru-Guzik, C. Allen
Nature Communications2023 , 14, 35 - Roughness of molecular property landscapes and its impact on modellability.
M. Aldeghi, D.E. Graff, N. Frey, J.A. Morrone, E.O. Pyzer-Knapp, K.E. Jordan, C.W. Coley
Journal of Chemical Information and Modeling2022 , 62(19), 4660−4671 - A graph representation of molecular ensembles for polymer property prediction.
M. Aldeghi, C.W. Coley
Chemical Science2022 , 13, 10486−10498 - A focus on simulation and machine learning as complementary tools for chemical space navigation.
M. Aldeghi, C.W. Coley
Chemical Science2022 , 13, 8221−8223 - Self-focusing virtual screening with active design space pruning.
D.E. Graff, M. Aldeghi, J.A. Morrone, K.E. Jordan, E.O. Pyzer-Knapp, C.W. Coley
Journal of Chemical Information and Modeling2022 , 62(16), 3854−3862 - On scientific understanding with artificial intelligence.
M. Krenn, R. Pollice, S.Y. Guo, M. Aldeghi, A. Cervera-Lierta, P. Friederich, G.P. Gomes, F. Häse, A. Jinich, A. Nigam, Z. Yao, A. Aspuru-Guzik
Nature Reviews Physics2022 , DOI: 10.1038/s42254-022-00518-3 - Bayesian optimization with known experimental and design constraints for chemistry applications.
R.J. Hickman*, M. Aldeghi*, F. Häse, A. Aspuru-Guzik
Digital Discovery2022 , 1, 732−744 - Self-driving platform for metal nanoparticle synthesis: combining microfluidics and machine learning.
H. Tao, T. Wu, S. Kheiri, M. Aldeghi, A. Aspuru-Guzik, E. Kumacheva
Advanced Functional Materials2021 , 2106725 - Nanoparticle synthesis assisted by machine learning.
H. Tao, T. Wu, M. Aldeghi, T.C. Wu, A. Aspuru-Guzik, E. Kumacheva
Nature Reviews Materials2021 , 6, 701−716 - Alchemical absolute protein-ligand binding free energies for drug design.
Y. Khalak, G. Tresadern, M. Aldeghi, H.M. Baumann, D.L. Mobley, B.L. de Groot, V. Gapsys
Chemical Science2021 , 12, 13958−13971 - Gryffin: An algorithm for Bayesian optimization of categorical variables informed by expert knowledge.
F. Häse, M. Aldeghi, R.J. Hickman, L.M. Roch, A. Aspuru-Guzik
Applied Physics Reviews2021 , 8, 031406 - Machine learning directed drug formulation development.
P. Bannigan, M. Aldeghi, Z. Bao, F. Häse, A. Aspuru-Guzik, C. Allen
Advanced Drug Delivery Reviews2021 , 175, 113806 - Accurate absolute free energies for ligand–protein binding based on non-equilibrium approaches.
V. Gapsys, A. Yildirim, M. Aldeghi, Y. Khalak, D. van der Spoel, B.L. de Groot
Communications Chemistry2021 , 4(61), 1−13 - Golem: An algorithm for robust experiment and process optimization.
M. Aldeghi, F. Häse, R.J. Hickman, I. Tamblyn, A. Aspuru-Guzik
Chemical Science2021 , 12, 14792−14807 - Assigning Confidence to Molecular Property Prediction.
A. Nigam, R. Pollice, M.F.D. Hurley, R.J. Hickman, M. Aldeghi, N. Yoshikawa, S. Chithrananda, V.A. Voelz, A. Aspuru-Guzik
Expert Opinion on Drug Discovery2021 , 16(9), 1009−1023 - Data-driven strategies for accelerated materials design.
R. Pollice, G.P. Gomes, M. Aldeghi, R.J. Hickman, M. Krenn, C. Lavigne, M.L. D'Addario, A. Nigam, C.T. Ser, Z. Yao, A. Aspuru-Guzik
Accounts of Chemical Research2021 , 54(4), 849−860 - Olympus: a benchmarking framework for noisy optimization and experiment planning.
F. Häse*, M. Aldeghi*, R.J. Hickman, L.M. Roch, M. Christensen, E. Liles, J.E. Hein, A. Aspuru-Guzik
Machine Learning: Science and Technology2021 , 2, 035021 - Structural basis for antibiotic action of the B1 antivitamin 2'-methoxy-thiamine.
F.R. von Pappenheim*, M. Aldeghi*, B. Shome, T. Begley, B.L. de Groot, K. Tittmann
Nature Chemical Biology2020 , 16, 1237−1245 - Characterising inter-helical interactions of G protein-coupled receptors with the fragment molecular orbital method.
A. Heifetz, I. Morao, M.M. Babu, T. James, M.W.Y. Southey, D.G. Fedorov, M. Aldeghi, M.J. Bodkin, A. Townsend-Nicholson
Journal of Chemical Theory and Computation2020 , 16(4), 2814−2824 - Large scale relative protein ligand binding affinities using non-equilibrium alchemy.
V. Gapsys, L. Pérez-Benito, M. Aldeghi, D. Seeliger, H. Van Vlijmen, G. Tresdern, B.L. de Groot
Chemical Science2020 , 11, 1140−1152 - The SAMPL6 SAMPLing challenge: Assessing the reliability and efficiency of binding free energy calculations.
A. Rizzi, T. Jensen, D.R. Slochower, M. Aldeghi, V. Gapsys, D. Ntekoumes, S. Bosisio, M. Papadourakis, N.M. Henriksen, B.L. de Groot, Z. Cournia, A. Dickson, J. Michel, M.K. Gilson, M.R. Shirts, D.L. Mobley, J.D. Chodera
Journal of Computer-Aided Molecular Design2020 , 34, 601−633 - Predicting kinase inhibitor resistance: physics-based and data-driven approaches.
M. Aldeghi, V. Gapsys, B.L. de Groot
ACS Central Science2019 , 5(8), 1468−1474 - A molecular mechanism for transthyretin amyloidogenesis.
A.W. Yee*, M. Aldeghi*, M. Blakeley, A. Ostermann, P. Mas, M. Moulin, D. de Sanctis, M.W. Bowler, C. Mueller-Dieckmann, E. Mitchell, M. Haertlein, B.L. de Groot, E. Boeri Erba, V.T. Forsyth
Nature Communications2019 , 10, 925 - Characterising GPCR–ligand interactions using a fragment molecular orbital-based approach.
A. Heifetz, T. James, M. Southey, I. Morao, M. Aldeghi, L. Sarrat, D.G. Fedorov, M.J. Bodkin, A. Townsend-Nicholson
Current Opinion in Structural Biology2019 , 55, 85−92 - Accurate calculation of free energy changes upon amino acid mutation.
M. Aldeghi, B.L. de Groot, V. Gapsys
In: Computational Methods in Protein Evolution (Ed. T. Sikosek)
Methods in Molecular Biology. Humana Press2019 , Vol. 1851, Chap. 2, 19−47 - Accurate estimation of ligand binding affinity changes upon protein mutation.
M. Aldeghi, V. Gapsys, B.L. de Groot
ACS Central Science2018 , 4(12), 1708−1718 - Absolute alchemical free energy calculations for ligand binding: a beginner’s guide.
M. Aldeghi, J.P. Bluck, P.C. Biggin
In: Computational Drug Discovery and Design (Ed. M. Gore, U. Jagtap)
Methods in Molecular Biology. Humana Press2018 , Vol. 1762, Chap. 11, 199−232 - Large-scale analysis of water stability in bromodomain binding pockets with grand canonical Monte Carlo.
M. Aldeghi, G.A. Ross, M.J. Bodkin, J.W. Essex, S. Knapp, P.C. Biggin
Communications Chemistry2018 , 1, 19 - Exploring GPCR-ligand interactions with the Fragment Molecular Orbital (FMO) method.
E.I. Chudyk, L. Sarrat, M. Aldeghi, D.G. Fedorov, M.J. Bodkin, T. James, M. Southey, R. Robinson, I. Morao, A. Heifetz
In: Computational Methods for GPCR Drug Discovery (Ed. A. Heifetz)
Methods in Molecular Biology. Humana Press2018 , Vol. 1705, Chap. 8, 179−195 - Statistical analysis on the performance of Molecular Mechanics Poisson-Boltzmann Surface Area versus absolute binding free energy calculations: bromodomains as a case study.
M. Aldeghi, M.J. Bodkin, S. Knapp, P.C. Biggin
Journal of Chemical Information and Modeling2017 , 57(9), 2203−2221 - Advances in molecular simulation.
M. Aldeghi, P.C. Biggin
In: Comprehensive Medicinal Chemistry III (Ed. S. Chackalamannil, D. Rotella, S. Ward)
Reference Module in Chemistry, Molecular Sciences and Chemical Engineering. Elsevier2017 , Vol. 3, Chap. 2, 14−33 - Predictions of ligand selectivity from absolute binding free energy calculations.
M. Aldeghi, A. Heiftez, M.J. Bodkin, S. Knapp, P.C. Biggin
Journal of the American Chemical Society2017 , 139(2), 946−957 - Using the fragment molecular orbital method to investigate agonist–orexin-2 receptor interactions.
A. Heifetz, M. Aldeghi, E.I. Chudyk, D.G. Fedorov, M.J. Bodkin, P.C. Biggin
Biochemical Society Transactions2016 , 44(2), 574−581 - Fragment molecular orbital method applied to lead optimization of novel Interleukin-2 Inducible T-Cell Kinase (ITK) inhibitors.
A. Heifetz, G. Trani, M. Aldeghi, C.H. MacKinnon, P.A. McEwan, F.A. Brookfield, E.I. Chudyk, M.J. Bodkin, Z. Pei, J.D. Burch, D.F. Ortwine
Journal of Medicinal Chemistry2016 , 59(9), 4352−4363 - Application of an integrated GPCR SAR-Modelling platform to explain the activation selectivity of human 5-HT2C over 5-HT2B.
A. Heifetz, R.I. Storer, G. McMurray, T. James, I. Morao, M. Aldeghi, M.J. Bodkin, P.C. Biggin
ACS Chemical Biology2016 , 11(5), 1372−1382 - Beyond membrane protein structure: drug discovery, dynamics and difficulties.
P.C. Biggin, M. Aldeghi, M.J. Bodkin, A. Heiftez
In: The Next Generation in Membrane Protein Structure Determination (Ed. I. Moraes)
Advances in Experimental Medicine and Biology. Springer2016 , Chap. 11, 450−461 - Accurate calculation of the absolute free energy of binding for drug molecules.
M. Aldeghi, A. Heiftez, M.J. Bodkin, S. Knapp, P.C. Biggin
Chemical Science2016 , 7, 207−218 - The Fragment Molecular Orbital method reveals new insight into the chemical nature of GPCR-ligand interactions.
A. Heiftez, E.I. Chudyk, L. Gleave, M. Aldeghi, V. Cherezov, D.G. Fedorov, P.C. Biggin, M.J. Bodkin
Journal of Chemical Information and Modeling2015 , 56(1), 159−172 - Selective targeting of the BRG/PB1 bromodomains impairs embryonic and trophoblast stem cell maintenance.
O. Fedorov, J. Castex, C. Tallant, D. R. Owen, S. Martin, M. Aldeghi, O. Monteiro, P. Filippakopoulos, S. Picaud, J. D. Trzupek, B. S. Gerstenberger, C. Bountra, D. Willmann, C. Wells, M. Philpott, C. Rogers, P. C. Biggin, P. E. Brennan, M. E. Bunnage, R. Schüle, T. Günther, S. Knapp, S. Müller
Science Advances2015 , 1 - Two and Three-dimensional Rings in Drugs.
M. Aldeghi, S. Malhotra, D.L. Selwood, A.W.E. Chan
Chemical Biology & Drug Design2014 , 83, 450−461
* denotes equal contribution
- Practically significant method comparison protocols for machine learning in small molecule drug discovery.
-
Bayer
- Practically significant method comparison protocols for machine learning in small molecule drug discovery.
J.R. Ash, C. Wognum, R. Rodríguez-Pérez, M. Aldeghi, A.C, Cheng, D. Clevert, O. Engkvist, C. Fang, D.J. Price, J.M. Hughes-Oliver, W.P. Walters
ChemRxiv2024 - A call for an industry-led initiative to critically assess machine learning for real-world drug discovery.
C. Wognum, J.R. Ash, M. Aldeghi, R. Rodríguez-Pérez, C. Fang, A.C. Cheng, D.J. Price, D. Clevert, O. Engkvist, W.P. Walters
Nature Machine Intelligence2024 , 6, 1120−1121
- Practically significant method comparison protocols for machine learning in small molecule drug discovery.
-
Google
- Olympus, enhanced: benchmarking mixed-parameter and multi-objective optimization in chemistry and materials science.
R. Hickman, P. Parakh, A. Cheng, Q. Ai, J. Schrier, M. Aldeghi, A. Aspuru-Guzik
ChemRxiv2023
- Olympus, enhanced: benchmarking mixed-parameter and multi-objective optimization in chemistry and materials science.
-
MIT
- Roughness of molecular property landscapes and its impact on modellability.
M. Aldeghi, D.E. Graff, N. Frey, J.A. Morrone, E.O. Pyzer-Knapp, K.E. Jordan, C.W. Coley
Journal of Chemical Information and Modeling2022 , 62(19), 4660−4671 - A graph representation of molecular ensembles for polymer property prediction.
M. Aldeghi, C.W. Coley
Chemical Science2022 , 13, 10486−10498 - A focus on simulation and machine learning as complementary tools for chemical space navigation.
M. Aldeghi, C.W. Coley
Chemical Science2022 , 13, 8221−8223 - Self-focusing virtual screening with active design space pruning.
D.E. Graff, M. Aldeghi, J.A. Morrone, K.E. Jordan, E.O. Pyzer-Knapp, C.W. Coley
Journal of Chemical Information and Modeling2022 , 62(16), 3854−3862
- Roughness of molecular property landscapes and its impact on modellability.
-
Vector/Toronto
- Anubis: Bayesian optimization with unknown feasibility constraints for scientific experimentation.
R. Hickman, M. Aldeghi, A. Aspuru-Guzik
ChemRxiv2023 - Atlas: A Brain for Self-driving Laboratories.
R. Hickman, M. Sim, S. Pablo-García, I. Woolhouse, H. Hao, Z. Bao, P. Bannigan, C. Allen, M. Aldeghi, A. Aspuru-Guzik
ChemRxiv2023 - Machine learning models to accelerate the design of polymeric long-acting injectables.
P. Bannigan, Z. Bao, R.J. Hickman, M. Aldeghi, F. Häse, A. Aspuru-Guzik, C. Allen
Nature Communications2023 , 14, 35 - On scientific understanding with artificial intelligence.
M. Krenn, R. Pollice, S.Y. Guo, M. Aldeghi, A. Cervera-Lierta, P. Friederich, G.P. Gomes, F. Häse, A. Jinich, A. Nigam, Z. Yao, A. Aspuru-Guzik
Nature Reviews Physics2022 , DOI: 10.1038/s42254-022-00518-3 - Bayesian optimization with known experimental and design constraints for chemistry applications.
R.J. Hickman*, M. Aldeghi*, F. Häse, A. Aspuru-Guzik
Digital Discovery2022 , 1, 732−744 - Self-driving platform for metal nanoparticle synthesis: combining microfluidics and machine learning.
H. Tao, T. Wu, S. Kheiri, M. Aldeghi, A. Aspuru-Guzik, E. Kumacheva
Advanced Functional Materials2021 , 2106725 - Nanoparticle synthesis assisted by machine learning.
H. Tao, T. Wu, M. Aldeghi, T.C. Wu, A. Aspuru-Guzik, E. Kumacheva
Nature Reviews Materials2021 , , 6, 701−716 - Gryffin: An algorithm for Bayesian optimization of categorical variables informed by expert knowledge.
F. Häse, M. Aldeghi, R.J. Hickman, L.M. Roch, A. Aspuru-Guzik
Applied Physics Reviews2021 , 8, 031406 - Machine learning directed drug formulation development.
P. Bannigan, M. Aldeghi, Z. Bao, F. Häse, A. Aspuru-Guzik, C. Allen
Advanced Drug Delivery Reviews2021 , , 175, 113806 - Golem: An algorithm for robust experiment and process optimization.
M. Aldeghi, F. Häse, R.J. Hickman, I. Tamblyn, A. Aspuru-Guzik
Chemical Science2021 , 12, 14792−14807 - Assigning Confidence to Molecular Property Prediction.
A. Nigam, R. Pollice, M.F.D. Hurley, R.J. Hickman, M. Aldeghi, N. Yoshikawa, S. Chithrananda, V.A. Voelz, A. Aspuru-Guzik
Expert Opinion on Drug Discovery2021 , 16(9), 1009−1023 - Data-driven strategies for accelerated materials design.
R. Pollice, G.P. Gomes, M. Aldeghi, R.J. Hickman, M. Krenn, C. Lavigne, M.L. D'Addario, A. Nigam, C.T. Ser, Z. Yao, A. Aspuru-Guzik
Accounts of Chemical Research2021 , 54(4), 849−860 - Olympus: a benchmarking framework for noisy optimization and experiment planning.
F. Häse*, M. Aldeghi*, R.J. Hickman, L.M. Roch, M. Christensen, E. Liles, J.E. Hein, A. Aspuru-Guzik
Machine Learning: Science and Technology2021 , 2, 035021
- Anubis: Bayesian optimization with unknown feasibility constraints for scientific experimentation.
-
Max Planck
- Prospective evaluation of structure-based simulations reveal their ability to predict the impact of kinase mutations on inhibitor binding.
S. Singh, V. Gapsys, M. Aldeghi, D. Schaller, A.M. Rangwala, J.B. White, J.P. Bluck, J. Scheen, W.G. Glass, J. Guo, S. Hayat, B.L. de Groot, A. Volkamer, C.D. Christ, M.A. Seeliger, J.D. Chodera
bioRxiv2024 - Alchemical absolute protein-ligand binding free energies for drug design.
Y. Khalak, G. Tresadern, M. Aldeghi, H.M. Baumann, D.L. Mobley, B.L. de Groot, V. Gapsys
Chemical Science2021 , 12, 13958-13971 - Accurate absolute free energies for ligand–protein binding based on non-equilibrium approaches.
V. Gapsys, A. Yildirim, M. Aldeghi, Y. Khalak, D. van der Spoel, B.L. de Groot
Communications Chemistry2021 , 4(61), 1−13 - Structural basis for antibiotic action of the B1 antivitamin 2'-methoxy-thiamine.
F.R. von Pappenheim*, M. Aldeghi*, B. Shome, T. Begley, B.L. de Groot, K. Tittmann
Nature Chemical Biology2020 , 16, 1237−1245 - Characterising inter-helical interactions of G protein-coupled receptors with the fragment molecular orbital method.
A. Heifetz, I. Morao, M.M. Babu, T. James, M.W.Y. Southey, D.G. Fedorov, M. Aldeghi, M.J. Bodkin, A. Townsend-Nicholson
Journal of Chemical Theory and Computation2020 , 16(4), 2814−2824 - Large scale relative protein ligand binding affinities using non-equilibrium alchemy.
V. Gapsys, L. Pérez-Benito, M. Aldeghi, D. Seeliger, H. Van Vlijmen, G. Tresdern, B.L. de Groot
Chemical Science2020 , 11, 1140−1152 - The SAMPL6 SAMPLing challenge: Assessing the reliability and efficiency of binding free energy calculations.
A. Rizzi, T. Jensen, D.R. Slochower, M. Aldeghi, V. Gapsys, D. Ntekoumes, S. Bosisio, M. Papadourakis, N.M. Henriksen, B.L. de Groot, Z. Cournia, A. Dickson, J. Michel, M.K. Gilson, M.R. Shirts, D.L. Mobley, J.D. Chodera
Journal of Computer-Aided Molecular Design2020 , 34, 601−633 - Predicting kinase inhibitor resistance: physics-based and data-driven approaches.
M. Aldeghi, V. Gapsys, B.L. de Groot
ACS Central Science2019 , 5(8), 1468−1474 - A molecular mechanism for transthyretin amyloidogenesis.
A.W. Yee*, M. Aldeghi*, M. Blakeley, A. Ostermann, P. Mas, M. Moulin, D. de Sanctis, M.W. Bowler, C. Mueller-Dieckmann, E. Mitchell, M. Haertlein, B.L. de Groot, E. Boeri Erba, V.T. Forsyth
Nature Communications2019 , 10, 925 - Characterising GPCR–ligand interactions using a fragment molecular orbital-based approach.
A. Heifetz, T. James, M. Southey, I. Morao, M. Aldeghi, L. Sarrat, D.G. Fedorov, M.J. Bodkin, A. Townsend-Nicholson
Current Opinion in Structural Biology2019 , 55, 85−92 - Accurate calculation of free energy changes upon amino acid mutation.
M. Aldeghi, B.L. de Groot, V. Gapsys
In: Computational Methods in Protein Evolution (Ed. T. Sikosek)
Methods in Molecular Biology. Humana Press2019 , Vol. 1851, Chap. 2, 19−47 - Accurate estimation of ligand binding affinity changes upon protein mutation.
M. Aldeghi, V. Gapsys, B.L. de Groot
ACS Central Science2018 , 4(12), 1708−1718
- Prospective evaluation of structure-based simulations reveal their ability to predict the impact of kinase mutations on inhibitor binding.
-
Oxford
- Absolute alchemical free energy calculations for ligand binding: a beginner’s guide.
M. Aldeghi, J.P. Bluck, P.C. Biggin
In: Computational Drug Discovery and Design (Ed. M. Gore, U. Jagtap)
Methods in Molecular Biology. Humana Press2018 , Vol. 1762, Chap. 11, 199−232 - Large-scale analysis of water stability in bromodomain binding pockets with grand canonical Monte Carlo.
M. Aldeghi, G.A. Ross, M.J. Bodkin, J.W. Essex, S. Knapp, P.C. Biggin
Communications Chemistry2018 , 1, 19 - Exploring GPCR-ligand interactions with the Fragment Molecular Orbital (FMO) method.
E.I. Chudyk, L. Sarrat, M. Aldeghi, D.G. Fedorov, M.J. Bodkin, T. James, M. Southey, R. Robinson, I. Morao, A. Heifetz
In: Computational Methods for GPCR Drug Discovery (Ed. A. Heifetz)
Methods in Molecular Biology. Humana Press2018 , Vol. 1705, Chap. 8, 179−195 - Statistical analysis on the performance of Molecular Mechanics Poisson-Boltzmann Surface Area versus absolute binding free energy calculations: bromodomains as a case study.
M. Aldeghi, M.J. Bodkin, S. Knapp, P.C. Biggin
Journal of Chemical Information and Modeling2017 , 57(9), 2203−2221 - Advances in molecular simulation.
M. Aldeghi, P.C. Biggin
In: Comprehensive Medicinal Chemistry III (Ed. S. Chackalamannil, D. Rotella, S. Ward)
Reference Module in Chemistry, Molecular Sciences and Chemical Engineering. Elsevier2017 , Vol. 3, Chap. 2, 14−33 - Predictions of ligand selectivity from absolute binding free energy calculations.
M. Aldeghi, A. Heiftez, M.J. Bodkin, S. Knapp, P.C. Biggin
Journal of the American Chemical Society2017 , 139(2), 946−957 - Using the fragment molecular orbital method to investigate agonist–orexin-2 receptor interactions.
A. Heifetz, M. Aldeghi, E.I. Chudyk, D.G. Fedorov, M.J. Bodkin, P.C. Biggin
Biochemical Society Transactions2016 , 44(2), 574−581 - Fragment molecular orbital method applied to lead optimization of novel Interleukin-2 Inducible T-Cell Kinase (ITK) inhibitors.
A. Heifetz, G. Trani, M. Aldeghi, C.H. MacKinnon, P.A. McEwan, F.A. Brookfield, E.I. Chudyk, M.J. Bodkin, Z. Pei, J.D. Burch, D.F. Ortwine
Journal of Medicinal Chemistry2016 , 59(9), 4352−4363 - Application of an integrated GPCR SAR-Modelling platform to explain the activation selectivity of human 5-HT2C over 5-HT2B.
A. Heifetz, R.I. Storer, G. McMurray, T. James, I. Morao, M. Aldeghi, M.J. Bodkin, P.C. Biggin
ACS Chemical Biology2016 , 11(5), 1372−1382 - Beyond membrane protein structure: drug discovery, dynamics and difficulties.
P.C. Biggin, M. Aldeghi, M.J. Bodkin, A. Heiftez
In: The Next Generation in Membrane Protein Structure Determination (Ed. I. Moraes)
Advances in Experimental Medicine and Biology. Springer2016 , Chap. 11, 450−461 - Accurate calculation of the absolute free energy of binding for drug molecules.
M. Aldeghi, A. Heiftez, M.J. Bodkin, S. Knapp, P.C. Biggin
Chemical Science2016 , 7, 207−218 - The Fragment Molecular Orbital method reveals new insight into the chemical nature of GPCR-ligand interactions.
A. Heiftez, E.I. Chudyk, L. Gleave, M. Aldeghi, V. Cherezov, D.G. Fedorov, P.C. Biggin, M.J. Bodkin
Journal of Chemical Information and Modeling2015 , 56(1), 159−172 - Selective targeting of the BRG/PB1 bromodomains impairs embryonic and trophoblast stem cell maintenance.
O. Fedorov, J. Castex, C. Tallant, D. R. Owen, S. Martin, M. Aldeghi, O. Monteiro, P. Filippakopoulos, S. Picaud, J. D. Trzupek, B. S. Gerstenberger, C. Bountra, D. Willmann, C. Wells, M. Philpott, C. Rogers, P. C. Biggin, P. E. Brennan, M. E. Bunnage, R. Schüle, T. Günther, S. Knapp, S. Müller
Science Advances2015 , 1
- Absolute alchemical free energy calculations for ligand binding: a beginner’s guide.
-
UCL
- Two and Three-dimensional Rings in Drugs.
M. Aldeghi, S. Malhotra, D.L. Selwood, A.W.E. Chan
Chemical Biology & Drug Design2014 , 83, 450−461
- Two and Three-dimensional Rings in Drugs.