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Merged
carlocamilloni merged 1 commit into from
Jan 30, 2026
Merged

new guess for the interaction model based #608

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34 changes: 19 additions & 15 deletions src/multiego/ensemble.py
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Original file line number Diff line number Diff line change
Expand Up @@ -352,7 +352,6 @@ def init_meGO_matrices(ensemble, args, custom_dict):

# TODO: probabilities of equivalent atoms should be averaged


reference_contact_matrices[name] = reference_contact_matrices[name].add_prefix("rc_")
reference_contact_matrices[name]["c6_i"] = [lj_data_dict[x][0] for x in reference_contact_matrices[name]["rc_ai"]]
reference_contact_matrices[name]["c6_j"] = [lj_data_dict[x][0] for x in reference_contact_matrices[name]["rc_aj"]]
Expand Down Expand Up @@ -1038,8 +1037,10 @@ def set_sig_epsilon(meGO_LJ, parameters):
# These are defined only if the training probability is greater than MD_threshold and
# by comparing them with RC_probabilities so that the resulting epsilon is between eps_min and eps_0
condition = (
meGO_LJ["probability"] > meGO_LJ["limit_rc_att"] * np.maximum(meGO_LJ["rc_probability"], meGO_LJ["rc_threshold"])
) & (meGO_LJ["probability"] > meGO_LJ["md_threshold"]) & (meGO_LJ["epsilon_prior"] > 0.)
(meGO_LJ["probability"] > meGO_LJ["limit_rc_att"] * np.maximum(meGO_LJ["rc_probability"], meGO_LJ["rc_threshold"]))
& (meGO_LJ["probability"] > meGO_LJ["md_threshold"])
& (meGO_LJ["epsilon_prior"] > 0.0)
)

meGO_LJ.loc[condition, "epsilon"] = np.maximum(0.0, meGO_LJ["epsilon_prior"]) - (
(meGO_LJ["epsilon_0"] - np.maximum(0.0, meGO_LJ["epsilon_prior"])) / np.log(meGO_LJ["rc_threshold"])
Expand All @@ -1048,20 +1049,23 @@ def set_sig_epsilon(meGO_LJ, parameters):
meGO_LJ.loc[condition, "sigma"] = meGO_LJ["distance"] / 2.0 ** (1.0 / 6.0)

condition = (
meGO_LJ["probability"] > meGO_LJ["limit_rc_att"] * np.maximum(meGO_LJ["rc_probability"], meGO_LJ["rc_threshold"])
) & (meGO_LJ["probability"] > meGO_LJ["md_threshold"]) & (meGO_LJ["epsilon_prior"] < 0.) & (meGO_LJ["rc_probability"] > meGO_LJ["md_threshold"])
(meGO_LJ["probability"] > meGO_LJ["limit_rc_att"] * np.maximum(meGO_LJ["rc_probability"], meGO_LJ["rc_threshold"]))
& (meGO_LJ["probability"] > meGO_LJ["md_threshold"])
& (meGO_LJ["epsilon_prior"] < 0.0)
& (meGO_LJ["rc_probability"] > meGO_LJ["md_threshold"])
)
meGO_LJ.loc[condition, "epsilon"] = (-meGO_LJ["rep"] * (meGO_LJ["distance"] / meGO_LJ["rc_distance"]) ** 12).clip(
lower=-20 * meGO_LJ["rep"], upper=-0.05 * meGO_LJ["rep"]
)[condition]
meGO_LJ.loc[condition, "learned"] = 1

#condition = (
# condition = (
# meGO_LJ["probability"] > meGO_LJ["limit_rc_att"] * np.maximum(meGO_LJ["rc_probability"], meGO_LJ["rc_threshold"])
#) & (meGO_LJ["probability"] > meGO_LJ["md_threshold"]) & (meGO_LJ["epsilon_prior"] < 0.) & (meGO_LJ["rc_probability"] <= meGO_LJ["md_threshold"])
#meGO_LJ.loc[condition, "epsilon"] = -meGO_LJ["rep"] * (
# ) & (meGO_LJ["probability"] > meGO_LJ["md_threshold"]) & (meGO_LJ["epsilon_prior"] < 0.) & (meGO_LJ["rc_probability"] <= meGO_LJ["md_threshold"])
# meGO_LJ.loc[condition, "epsilon"] = -meGO_LJ["rep"] * (
# 1.0 + (np.maximum(meGO_LJ["rc_probability"], meGO_LJ["rc_threshold"]) - meGO_LJ["probability"])
#)
#meGO_LJ.loc[condition, "learned"] = 1
# )
# meGO_LJ.loc[condition, "learned"] = 1

# Not-attractive interactions
# this is used only when MD_th < MD_p < limit_rc_att*RC_p
Expand All @@ -1076,15 +1080,15 @@ def set_sig_epsilon(meGO_LJ, parameters):
)[condition]
meGO_LJ.loc[condition, "learned"] = 1

#condition = (
# condition = (
# (meGO_LJ["probability"] <= meGO_LJ["limit_rc_att"] * np.maximum(meGO_LJ["rc_probability"], meGO_LJ["rc_threshold"]))
# & (meGO_LJ["probability"] > meGO_LJ["md_threshold"])
# & (meGO_LJ["rc_probability"] <= meGO_LJ["md_threshold"])
#)
#meGO_LJ.loc[condition, "epsilon"] = -meGO_LJ["rep"] * (
# )
# meGO_LJ.loc[condition, "epsilon"] = -meGO_LJ["rep"] * (
# 1.0 + (np.maximum(meGO_LJ["rc_probability"], meGO_LJ["rc_threshold"]) - meGO_LJ["probability"])
#)
#meGO_LJ.loc[condition, "learned"] = 1
# )
# meGO_LJ.loc[condition, "learned"] = 1

# 1-4 interactions are special and cannot become attractive because they are part of the bonded-interactions
condition = (meGO_LJ["bond_distance"] < 4) & (meGO_LJ["same_chain"])
Expand Down
173 changes: 90 additions & 83 deletions src/multiego/resources/type_definitions.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,19 +3,38 @@
import sys

mg_OO_c12_rep = 7.5e-7
mg_OMOM_c12_rep = 2.5e-5
mg_HH_c12_rep = 1.2e-8
mg_ON_c12_rep = 7.5e-7
mg_NN_c12_rep = 2.5e-5

mg_HO_sigma = 0.169500
mg_eps_ch3 = 0.10
mg_eps_HO = 0.13 # hydrogen bond strength
mg_eps_ch_aromatic = 0.13
mg_eps_ch2 = 0.10
mg_eps_pol_nz = 0.11
mg_eps_pol = 0.09
mg_eps_ch1 = 0.07
mg_eps_bkbn_O_CB = 0.10
mg_eps_HO = 0.12

eps_O = 0.085
eps_OM = 0.085
eps_OA = 0.13
eps_N = 0.085
eps_NT = 0.085
eps_NL = 0.085
eps_NR = 0.07
eps_NZ = 0.11
eps_NE = 0.085
eps_C = 0.085
eps_CH = 0.11
eps_CH1 = 0.085
eps_CAH = 0.085
eps_CH2 = 0.11
eps_CAH2 = 0.11
eps_CH3 = 0.12
eps_CH2r = 0.11
eps_S = 0.14
eps_CH3p = 0.00
eps_P = 0.00
eps_OE = 0.00
eps_CR1 = 0.00
eps_H = 0.00
eps_C0 = 0.00

# Dataframe with atom types and associated parameters
gromos_atp = pd.DataFrame(
Expand Down Expand Up @@ -75,56 +94,56 @@
],
# all to ch2 except ch e ch3
"mg_c12": [
4.0 * 0.27601**12 * mg_eps_pol, # "O", sig=0.27601
4.0 * 0.26259**12 * mg_eps_pol, # "OM", sig=0.26259
4.0 * 0.29548**12 * mg_eps_pol, # "OA", sig=0.29548
4.0 * 0.31365**12 * mg_eps_pol, # "N", sig=0.31365
4.0 * 0.35722**12 * mg_eps_pol, # "NT", sig=0.35722
4.0 * 0.31365**12 * mg_eps_pol, # "NL", sig=0.31365
4.0 * 0.33411**12 * mg_eps_pol, # "NR", sig=0.33411
4.0 * 0.31365**12 * mg_eps_pol_nz, # "NZ", sig=0.31365
4.0 * 0.31365**12 * mg_eps_pol, # "NE", sig=0.31365
4.0 * 0.35812**12 * mg_eps_pol, # "C", sig=0.35812
4.0 * 0.35812**12 * mg_eps_ch_aromatic, # "CH", sig=0.35812
4.0 * 0.44592**12 * mg_eps_ch1, # "CH1", sig=0.50192
4.0 * 0.44592**12 * mg_eps_ch1, # "CAH", sig=0.50192
4.0 * 0.40704**12 * mg_eps_ch2, # "CH2", sig=0.40704
4.0 * 0.40704**12 * mg_eps_ch2, # "CAH2", sig=0.40704
4.0 * 0.37479**12 * mg_eps_ch3, # "CH3", sig=0.37479
4.0 * 0.39547**12 * mg_eps_ch2, # "CH2r", sig=0.39547
4.0 * 0.33077**12 * mg_eps_pol, # "S", sig=0.33077
4.0 * 0.37479**12 * mg_eps_ch3, # "CH3p", sig=0.37479
4.0 * 0.33856**12 * mg_eps_pol, # "P", sig=0.33856
4.0 * 0.28492**12 * mg_eps_pol, # "OE", sig=0.28492
4.0 * 0.37412**12 * mg_eps_pol, # "CR1", sig=0.37412
4.0 * 0.000000000 * mg_eps_pol, # "H",
4.0 * 0.000000000 * mg_eps_pol, # "C0",
4.0 * 0.27601**12 * eps_O, # "O", sig=0.27601
4.0 * 0.26259**12 * eps_OM, # "OM", sig=0.26259
4.0 * 0.29548**12 * eps_OA, # "OA", sig=0.29548
4.0 * 0.31365**12 * eps_N, # "N", sig=0.31365
4.0 * 0.35722**12 * eps_NT, # "NT", sig=0.35722
4.0 * 0.31365**12 * eps_NL, # "NL", sig=0.31365
4.0 * 0.33411**12 * eps_NR, # "NR", sig=0.33411
4.0 * 0.31365**12 * eps_NZ, # "NZ", sig=0.31365
4.0 * 0.31365**12 * eps_NE, # "NE", sig=0.31365
4.0 * 0.35812**12 * eps_C, # "C", sig=0.35812
4.0 * 0.35812**12 * eps_CH, # "CH", sig=0.35812
4.0 * 0.44592**12 * eps_CH1, # "CH1", sig=0.50192
4.0 * 0.44592**12 * eps_CAH, # "CAH", sig=0.50192
4.0 * 0.40704**12 * eps_CH2, # "CH2", sig=0.40704
4.0 * 0.40704**12 * eps_CAH2, # "CAH2", sig=0.40704
4.0 * 0.37479**12 * eps_CH3, # "CH3", sig=0.37479
4.0 * 0.39547**12 * eps_CH2r, # "CH2r", sig=0.39547
4.0 * 0.33077**12 * eps_S, # "S", sig=0.33077
4.0 * 0.37479**12 * eps_CH3p, # "CH3p", sig=0.37479
4.0 * 0.33856**12 * eps_P, # "P", sig=0.33856
4.0 * 0.28492**12 * eps_OE, # "OE", sig=0.28492
4.0 * 0.37412**12 * eps_CR1, # "CR1", sig=0.37412
4.0 * 0.000000000 * eps_H, # "H",
4.0 * 0.000000000 * eps_C0, # "C0",
],
"mg_c6": [
4.0 * 0.27601**6 * mg_eps_pol, # "O",
4.0 * 0.26259**6 * mg_eps_pol, # "OM",
4.0 * 0.29548**6 * mg_eps_pol, # "OA",
4.0 * 0.31365**6 * mg_eps_pol, # "N",
4.0 * 0.35722**6 * mg_eps_pol, # "NT",
4.0 * 0.31365**6 * mg_eps_pol, # "NL",
4.0 * 0.33411**6 * mg_eps_pol, # "NR",
4.0 * 0.31365**6 * mg_eps_pol_nz, # "NZ",
4.0 * 0.31365**6 * mg_eps_pol, # "NE",
4.0 * 0.35812**6 * mg_eps_pol, # "C",
4.0 * 0.35812**6 * mg_eps_ch_aromatic, # "CH"
4.0 * 0.44592**6 * mg_eps_ch1, # "CH1"
4.0 * 0.44592**6 * mg_eps_ch1, # "CAH"
4.0 * 0.40704**6 * mg_eps_ch2, # "CH2"
4.0 * 0.40704**6 * mg_eps_ch2, # "CAH2"
4.0 * 0.37479**6 * mg_eps_ch3, # "CH3"
4.0 * 0.39547**6 * mg_eps_ch2, # "CH2r"
4.0 * 0.33077**6 * mg_eps_pol, # "S",
4.0 * 0.37479**6 * mg_eps_ch3, # "CH3p"
4.0 * 0.33856**6 * mg_eps_pol, # "P",
4.0 * 0.28492**6 * mg_eps_pol, # "OE",
4.0 * 0.37412**6 * mg_eps_pol, # "CR1",
4.0 * 0.00000000 * mg_eps_pol, # "H",
4.0 * 0.00000000 * mg_eps_pol, # "C0",
4.0 * 0.27601**6 * eps_O, # "O",
4.0 * 0.26259**6 * eps_OM, # "OM",
4.0 * 0.29548**6 * eps_OA, # "OA",
4.0 * 0.31365**6 * eps_N, # "N",
4.0 * 0.35722**6 * eps_NT, # "NT",
4.0 * 0.31365**6 * eps_NL, # "NL",
4.0 * 0.33411**6 * eps_NR, # "NR",
4.0 * 0.31365**6 * eps_NZ, # "NZ",
4.0 * 0.31365**6 * eps_NE, # "NE",
4.0 * 0.35812**6 * eps_C, # "C",
4.0 * 0.35812**6 * eps_CH, # "CH"
4.0 * 0.44592**6 * eps_CH1, # "CH1"
4.0 * 0.44592**6 * eps_CAH, # "CAH"
4.0 * 0.40704**6 * eps_CH2, # "CH2"
4.0 * 0.40704**6 * eps_CAH2, # "CAH2"
4.0 * 0.37479**6 * eps_CH3, # "CH3"
4.0 * 0.39547**6 * eps_CH2r, # "CH2r"
4.0 * 0.33077**6 * eps_S, # "S",
4.0 * 0.37479**6 * eps_CH3p, # "CH3p"
4.0 * 0.33856**6 * eps_P, # "P",
4.0 * 0.28492**6 * eps_OE, # "OE",
4.0 * 0.37412**6 * eps_CR1, # "CR1",
4.0 * 0.00000000 * eps_H, # "H",
4.0 * 0.00000000 * eps_C0, # "C0",
],
}
)
Expand Down Expand Up @@ -203,26 +222,26 @@ def lj14_generator(df):
polar_sbtype = ["O", "OA", "OM", "N", "NL", "NT", "NR", "NZ", "NE", "C", "S"]
hyd_sbtype = ["CH3", "CH2", "CH2r", "CH1", "CAH", "CAH2"]
special_non_local = [
#{
# "atomtypes": (["O", "OM"], ["O", "OM"]), # charged oxygen-oxygen repulsion
# "interaction": "rep",
# "sigma": None, # not needed for repulsion
# "epsilon": mg_OO_c12_rep,
#},
{
"atomtypes": (["O"], ["O"]), # charged oxygen-oxygen repulsion
"interaction": "rep",
"sigma": None, # not needed for repulsion
"epsilon": mg_OO_c12_rep,
},
#{
# "atomtypes": (["NZ", "NL"], ["NZ", "NL"]), # charged nitrogen-nitrogen repulsion
# "interaction": "rep",
# "sigma": None, # not needed for repulsion
# "epsilon": mg_NN_c12_rep,
#},
{
"atomtypes": (["NZ"], ["NZ"]), # charged nitrogen-nitrogen repulsion
"atomtypes": (["O"], ["OM"]), # charged oxygen-oxygen repulsion
"interaction": "rep",
"sigma": None, # not needed for repulsion
"epsilon": mg_OO_c12_rep,
},
{
"atomtypes": (["OM"], ["OM"]), # charged oxygen-oxygen repulsion
"interaction": "rep",
"sigma": None, # not needed for repulsion
"epsilon": mg_OMOM_c12_rep,
},
{
"atomtypes": (["NL"], ["NL"]), # charged nitrogen-nitrogen repulsion
"interaction": "rep",
"sigma": None, # not needed for repulsion
"epsilon": mg_NN_c12_rep,
Expand All @@ -233,36 +252,24 @@ def lj14_generator(df):
"sigma": None, # not needed for repulsion
"epsilon": mg_HH_c12_rep,
},
#{
# "atomtypes": (polar_sbtype, hyd_sbtype), # polar - hydrophobic repulsion
# "interaction": "att",
# "sigma": None, # not needed for repulsion
# "epsilon": 0.09,
#}, # If None use default rc c12 repulsion
{
"atomtypes": (["O", "OM", "OA"], ["H"]), # hydrogen bond attraction
"interaction": "att",
"sigma": mg_HO_sigma,
"epsilon": mg_eps_HO,
},
{
"atomtypes": (["NL"], ["N", "NT", "NL", "NR", "NZ", "NE", "C", "CH", "CH1", "CAH", "CH2", "CAH2", "CH3", "CH2r", "S"]), # hydrogen bond attraction
"atomtypes": (["NL"], ["NZ", "N", "NT", "NR", "NE", "C", "CH", "CH1", "CAH", "CH2", "CAH2", "CH3", "CH2r", "S"]),
"interaction": "rep",
"sigma": None,
"epsilon": None,
},
{
"atomtypes": (["OM"], ["O", "OM", "C", "CH", "CH1", "CAH", "CH2", "CAH2", "CH3", "CH2r", "S"]), # hydrogen bond attraction
"atomtypes": (["OM"], ["C", "CH", "CH1", "CAH", "CH2", "CAH2", "CH3", "CH2r", "S"]),
"interaction": "rep",
"sigma": None,
"epsilon": None,
},
#{
# "atomtypes": (["CH1", "CAH"], ["CH1", "CAH"]), # hydrogen bond attraction
# "interaction": "rep",
# "sigma": None,
# "epsilon": None,
#},
]

# List of amino acids and nucleic acids
Expand Down
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