update (add rank)

app.py

@@ -9,6 +9,7 @@ from src.about import (
     CITATION_BUTTON_TEXT,
 )
 from src.display.css_html_js import custom_css
+import copy 
 
 from src.envs import API, REPO_ID
 current_dir = os.path.dirname(os.path.realpath(__file__))
@@ -44,10 +45,35 @@ in-depth evaluation framework for protein foundation models, driving their devel
 
 def convert_to_float(df):
     columns = df.columns
-    for col in columns[1:]:
+    for col in columns[2:]:
         df[col] = df[col].astype('float')
     return df
+
+
+def assign_rank_and_get_sorted_csv(src_csv_path, tag_csv_path, ignore_num=0):
+    src_csv = pd.read_csv(src_csv_path)
+    float_csv = convert_to_float(copy.deepcopy(src_csv))
+    tag_csv = pd.read_csv(tag_csv_path)
+    rank_csv = pd.DataFrame()
+    
+    float_csv = float_csv[ignore_num:]
     
+    for col in tag_csv.columns:
+        tag = int(tag_csv[col].iloc[0])
+        if tag == 0:
+            continue
+        float_csv[col] *= tag
+        float_csv[col] = float_csv[col].fillna(value=1e12)
+        rank_csv[col] = float_csv[col].rank(method='min')
+    rank_csv['__sum_of_ranks'] = rank_csv.sum(axis=1)
+    src_csv.insert(loc=0, column='Rank', value=-1 * np.ones(len(src_csv)))
+    src_csv.loc[list(range(ignore_num, len(src_csv))), 'Rank'] = rank_csv['__sum_of_ranks'].rank(method='min')
+    sorted_csv = src_csv.sort_values(by=["Rank"])
+    if ignore_num >0 :
+        sorted_csv.loc[list(range(ignore_num)),'Rank'] = [np.nan] * ignore_num
+    return sorted_csv
+
+
 
 # ### Space initialisation
 
@@ -65,95 +91,94 @@ with demo:
     with gr.Tabs(elem_classes="tab-buttons") as tabs:
         with gr.TabItem("🏆 Inverse Folding Leaderboard", elem_id='inverse-folding-table', id=0,):
             with gr.Row():
-                inverse_folding_csv = pd.read_csv('data/inverse_folding.csv')
-                print(convert_to_float(inverse_folding_csv))
+                inverse_folding_csv = assign_rank_and_get_sorted_csv('data_link/inverse_folding.csv', 'data_rank/inverse_folding.csv')
                 inverse_folding_table = gr.components.DataFrame(
                     value=convert_to_float(inverse_folding_csv).values,
                     height=99999,
                     interactive=False,
                     headers=inverse_folding_csv.columns.to_list(),
-                    datatype=['markdown'] + (len(inverse_folding_csv.columns)-1) * ['number'],
+                    datatype=['number', 'markdown'] + (len(inverse_folding_csv.columns)-1) * ['number'],
                     
                 )
         with gr.TabItem("🏆 Structure Design Leaderboard", elem_id='structure-design-table', id=1,):
             with gr.Row():
-                structure_design_csv = pd.read_csv('data/structure_design.csv')
+                structure_design_csv = assign_rank_and_get_sorted_csv('data_link/structure_design.csv','data_rank/structure_design.csv', ignore_num=1)
                 structure_design_table = gr.components.DataFrame(
                     value=convert_to_float(structure_design_csv).values,
                     height=99999,
                     interactive=False,
                     headers=structure_design_csv.columns.to_list(),
-                    datatype=['markdown'] + (len(structure_design_csv.columns)-1) * ['number'],
+                    datatype=['number', 'markdown'] + (len(structure_design_csv.columns)-1) * ['number'],
                 )
         with gr.TabItem("🏆 Sequence Design Leaderboard", elem_id='sequence-design-table', id=2,):
             with gr.Row():
-                sequence_design_csv = pd.read_csv('data/sequence_design.csv')
+                sequence_design_csv = assign_rank_and_get_sorted_csv('data_link/sequence_design.csv', 'data_rank/sequence_design.csv', ignore_num=1)
                 sequence_design_table = gr.components.DataFrame(
                     value=convert_to_float(sequence_design_csv).values,
                     height=99999,
                     interactive=False,
                     headers=sequence_design_csv.columns.to_list(),
-                    datatype=['markdown'] + (len(sequence_design_csv.columns)-1) * ['number'],
+                    datatype=['number', 'markdown'] + (len(sequence_design_csv.columns)-1) * ['number'],
                 )
         with gr.TabItem("🏆 Sequence-Structure Co-Design Leaderboard", elem_id='co-design-table', id=3,):
             with gr.Row():
-                co_design_csv = pd.read_csv('data/co_design.csv')
+                co_design_csv = assign_rank_and_get_sorted_csv('data_link/co_design.csv', 'data_rank/co_design.csv', ignore_num=1)
                 co_design_table = gr.components.DataFrame(
                     value=convert_to_float(co_design_csv).values,
                     height=99999,
                     interactive=False,
                     headers=co_design_csv.columns.to_list(),
-                    datatype=['markdown'] + (len(co_design_csv.columns)-1) * ['number'],
+                    datatype=['number', 'markdown'] + (len(co_design_csv.columns)-1) * ['number'],
                 )
         with gr.TabItem("���� Motif Scaffolding Leaderboard", elem_id='motif-scaffolding-table', id=4,):
             with gr.Row():
-                motif_scaffolding_csv = pd.read_csv('data/motif_scaffolding.csv')
+                motif_scaffolding_csv = assign_rank_and_get_sorted_csv('data_link/motif_scaffolding.csv', 'data_rank/motif_scaffolding.csv')
                 motif_scaffolding_table = gr.components.DataFrame(
                     value=convert_to_float(motif_scaffolding_csv).values,
                     height=99999,
                     interactive=False,
                     headers=motif_scaffolding_csv.columns.to_list(),
-                    datatype=['markdown'] + (len(motif_scaffolding_csv.columns)-1) * ['number'],
+                    datatype=['number', 'markdown'] + (len(motif_scaffolding_csv.columns)-1) * ['number'],
                 )
         with gr.TabItem("🏆 Antibody Design Leaderboard", elem_id='antibody-design-table', id=5,):
             with gr.Row():
-                antibody_design_csv = pd.read_csv('data/antibody_design.csv')
+                antibody_design_csv = assign_rank_and_get_sorted_csv('data_link/antibody_design.csv', 'data_rank/antibody_design.csv', ignore_num=1)
                 antibody_design_table = gr.components.DataFrame(
                     value=convert_to_float(antibody_design_csv).values,
                     height=99999,
                     interactive=False,
                     headers=antibody_design_csv.columns.to_list(),
-                    datatype=['markdown'] + (len(antibody_design_csv.columns)-1) * ['number'],
+                    datatype=['number', 'markdown'] + (len(antibody_design_csv.columns)-1) * ['number'],
                 )
         with gr.TabItem("🏅 Protein Folding Leaderboard", elem_id='protein-folding-table', id=6,):
             with gr.Row():
-                protein_folding_csv = pd.read_csv('data/protein_folding.csv')
+                protein_folding_csv = assign_rank_and_get_sorted_csv('data_link/protein_folding.csv', 'data_rank/protein_folding.csv')
                 protein_folding_table = gr.components.DataFrame(
                     value=convert_to_float(protein_folding_csv).values,
                     height=99999,
                     interactive=False,
                     headers=protein_folding_csv.columns.to_list(),
-                    datatype=['markdown'] + (len(protein_folding_csv.columns)-1) * ['number'],
+                    datatype=['number', 'markdown'] + (len(protein_folding_csv.columns)-1) * ['number'],
                 )
         with gr.TabItem("🏅 Multi-State Prediction Leaderboard", elem_id='multi-state-prediction-table', id=7,):
             with gr.Row():
-                multi_state_prediction_csv = pd.read_csv('data/multi_state_prediction.csv')
+                multi_state_prediction_csv = assign_rank_and_get_sorted_csv('data_link/multi_state_prediction.csv', 'data_rank/multi_state_prediction.csv')
                 multi_state_prediction_table = gr.components.DataFrame(
                     value=convert_to_float(multi_state_prediction_csv).values,
                     height=99999,
                     interactive=False,
                     headers=multi_state_prediction_csv.columns.to_list(),
-                    datatype=['markdown'] + (len(multi_state_prediction_csv.columns)-1) * ['number'],
+                    datatype=['number', 'markdown'] + (len(multi_state_prediction_csv.columns)-1) * ['number'],
                 )
         with gr.TabItem("🏅 Conformation Prediction Leaderboard", elem_id='conformation-prediction-table', id=8,):
             with gr.Row():
-                conformation_prediction_csv = pd.read_csv('data/conformation_prediction.csv')
+                conformation_prediction_csv = assign_rank_and_get_sorted_csv('data_link/conformation_prediction.csv', 'data_rank/conformation_prediction.csv')
                 conformation_prediction_table = gr.components.DataFrame(
                     value=convert_to_float(conformation_prediction_csv).values,
                     height=99999,
                     interactive=False,
                     headers=conformation_prediction_csv.columns.to_list(),
-                    datatype=['markdown'] + (len(conformation_prediction_csv.columns)-1) * ['number'],
+                    datatype=['number', 'markdown'] + (len(conformation_prediction_csv.columns)-1) * ['number'],
                 )
 
 

data_link/co_design.csv

@@ -0,0 +1,6 @@
+Model,scTM (L=100) ↑,scRMSD (L=100) ↓,Max Clust. (L=100) ↑,Max TM (L=100) ↓,scTM (L=200) ↑,scRMSD (L=200) ↓,Max Clust. (L=200) ↑,Max TM (L=200) ↓,scTM (L=300) ↑,scRMSD (L=300) ↓,Max Clust. (L=300) ↑,Max TM (L=300) ↓,scTM (L=500) ↑,scRMSD (L=500) ↓,Max Clust. (L=500) ↑,Max TM (L=500) ↓
+<a href="https://www.rcsb.org/"> Native PDBs </a>,0.91,2.98,0.75,NaN,0.88,3.24,0.77,NaN,0.92,3.94,0.75,NaN,0.9,9.64,0.8,NaN
+<a href="https://github.com/RosettaCommons/protein_generator"> ProteinGenerator </a>,0.91,3.75,0.24,0.73,0.88,6.24,0.25,0.72,0.81,9.26,0.22,0.71,0.41,33.91,0.18,0.73
+<a href="https://github.com/ProteinDesignLab/protpardelle"> ProtPardelle* </a>,0.56,12.9,0.57,0.66,0.64,13.67,0.1,0.69,0.69,14.91,0.04,0.72,0.4,41.23,0.6,0.69
+<a href="https://github.com/jasonkyuyim/multiflow"> Multiflow </a>,0.96,1.1,0.33,0.71,0.95,1.61,0.42,0.71,0.96,2.14,0.58,0.71,0.83,8.48,0.67,0.68
+<a href="https://github.com/evolutionaryscale/esm"> ESM3 (1.4B)* </a>,0.72,13.8,0.64,0.41,0.63,21.18,0.63,0.61,0.59,25.5,0.52,0.73,0.54,33.7,0.37,0.77

data_link/conformation_prediction.csv

@@ -0,0 +1,21 @@
+Model,Pairwise RMSD,*RMSF,Pearson r on Pairwise RMSD ↑,Pearson r on *Global RMSF ↑,Pearson r on *Per target RMSF ↑,*RMWD ↓,MD PCA W2 ↓,Joint PCA W2 ↓,PC sim > 0.5% ↑,Weak contacts J ↑,Transient contacts J ↑,*Exposed residue J ↑,*Exposed MI matrix ρ ↑,CA break % ↓,CA clash % ↓,PepBond break % ↓
+MD iid,2.76,1.63,0.96,0.97,0.99,0.71,0.76,0.7,93.9,0.9,0.8,0.93,0.56,0,0.1,3.4
+MD 2.5 ns,1.54,0.98,0.89,0.85,0.85,2.21,1.57,1.93,36.6,0.62,0.45,0.64,0.24,0,0.1,3.4
+<a href="https://github.com/bjing2016/EigenFold"> EigenFold </a>,5.96,NaN,-0.04,NaN,NaN,NaN,2.35,7.96,12.2,0.36,0.18,NaN,NaN,0.7,9.6,NaN
+<a href="https://www.nature.com/articles/s41586-023-06832-9"> MSA-depth256</a>,0.84,0.53,0.25,0.34,0.59,3.63,1.83,2.9,29.3,0.3,0.28,0.33,0.06,0,0.2,5.9
+<a href="https://www.nature.com/articles/s41586-023-06832-9"> MSA-depth64</a>,2.03,1.51,0.24,0.3,0.57,4,1.87,3.32,18.3,0.38,0.27,0.38,0.12,0,0.2,8.4
+<a href="https://www.nature.com/articles/s41586-023-06832-9"> MSA-depth32</a>,5.71,7.96,0.07,0.17,0.53,6.12,2.5,5.67,17.1,0.39,0.24,0.36,0.15,0,0.5,13
+<a href="https://github.com/lujiarui/Str2Str">Str2Str-ODE (t=0.1)</a>,1.66,NaN,0.13,NaN,NaN,NaN,2.12,4.42,6.1,0.42,0.17,NaN,NaN,0,0.1,13.7
+<a href="https://github.com/lujiarui/Str2Str">Str2Str-ODE (t=0.3)</a>,3.15,NaN,0.12,NaN,NaN,NaN,2.23,4.75,9.8,0.41,0.17,NaN,NaN,0,0.1,14.8
+<a href="https://github.com/lujiarui/Str2Str">Str2Str-SDE (t=0.1)</a>,4.74,NaN,0.1,NaN,NaN,NaN,2.54,8.84,9.8,0.4,0.13,NaN,NaN,1.6,0.2,23
+<a href="https://github.com/lujiarui/Str2Str">Str2Str-SDE (t=0.3)</a>,7.54,NaN,0,NaN,NaN,NaN,3.29,12.28,7.3,0.35,0.13,NaN,NaN,1.5,0.2,21.4
+<a href="https://github.com/bjing2016/alphaflow">AlphaFlow-PDB</a>,2.58,1.2,0.27,0.46,0.81,2.96,1.66,2.6,37.8,0.44,0.33,0.42,0.18,0,0.2,6.6
+<a href="https://github.com/bjing2016/alphaflow">AlphaFlow-MD</a>,2.88,1.63,0.53,0.66,0.85,2.68,1.53,2.28,39,0.57,0.38,0.5,0.24,0,0.2,21.7
+<a href="https://github.com/bjing2016/alphaflow">ESMFlow-PDB</a>,3,1.68,0.14,0.27,0.71,4.2,1.77,3.54,28,0.42,0.29,0.41,0.16,0,0.6,5.4
+<a href="https://github.com/bjing2016/alphaflow">ESMFlow-MD</a>,3.34,2.13,0.19,0.3,0.76,3.63,1.54,3.15,25.6,0.51,0.33,0.47,0.21,0,0.3,10.9
+<a href="https://github.com/bytedance/ConfDiff">ConfDiff-Open-ClsFree</a>,3.68,2.12,0.4,0.54,0.83,2.92,1.5,2.54,46.3,0.54,0.33,0.47,0.21,0,1.2,5.7
+<a href="https://github.com/bytedance/ConfDiff">ConfDiff-Open-PDB</a>,2.9,1.43,0.38,0.51,0.82,2.97,1.57,2.51,34.1,0.47,0.34,0.43,0.18,0,0.9,5.7
+<a href="https://github.com/bytedance/ConfDiff">ConfDiff-Open-MD</a>,3.43,2.21,0.59,0.67,0.85,2.76,1.44,2.25,35.4,0.59,0.36,0.5,0.24,0,0.8,6.3
+<a href="https://github.com/bytedance/ConfDiff">ConfDiff-ESM-ClsFree</a>,4.04,2.84,0.31,0.43,0.82,3.82,1.72,3.06,37.8,0.54,0.31,0.47,0.18,0,1.8,4.3
+<a href="https://github.com/bytedance/ConfDiff">ConfDiff-ESM-PDB</a>,3.42,2.06,0.29,0.4,0.8,3.67,1.7,3.17,34.1,0.48,0.31,0.42,0.18,0,1.6,3.9
+<a href="https://github.com/bytedance/ConfDiff">ConfDiff-ESM-MD</a>,3.91,2.79,0.35,0.48,0.82,3.67,1.66,2.89,39,0.56,0.34,0.48,0.23,0,1.5,4

data_link/inverse_folding.csv

@@ -0,0 +1,5 @@
+Model,CASP AAR ↑,CAMEO AAR ↑,scTM (L=100) ↑,pLDDT (L=100) ↑,scTM (L=200) ↑,pLDDT (L=200) ↑,scTM (L=300) ↑,pLDDT (L=300) ↑,scTM (L=400) ↑,pLDDT (L=400) ↑,scTM (L=500) ↑,pLDDT (L=500) ↑
+<a href="https://github.com/dauparas/ProteinMPNN">ProteinMPNN</a>,0.45,0.468,0.962,94.14,0.945,89.34,0.962,90.28,0.875,83.76,0.568,67.09
+<a href="https://github.com/facebookresearch/esm">ESM-IF1</a>,NaN,NaN,0.81,88.83,0.635,69.67,0.336,74.36,0.449,64.59,0.462,58.97
+<a href="https://github.com/BytedProtein/ByProt">LM-Design</a>,0.516,0.57,0.834,78.45,0.373,58.41,0.481,69.86,0.565,59.87,0.397,56.35
+<a href="https://github.com/evolutionaryscale/esm">ESM3</a>,NaN,NaN,0.942,86.6,0.486,60.69,0.632,70.78,0.564,62.63,0.452,59.37

data_link/motif_scaffolding.csv

@@ -0,0 +1,7 @@
+Model,1QJG,2KL8,4JHW,4ZYP,5IUS,5TPN,5TRV,5WN9,6EXZ,7MRX,3IXT,1BCF,1PRW,1YCR,5YUI,6E6R
+<a href="https://github.com/microsoft/protein-frame-flow"> FrameFlow </a>,15,100,10,30,80,60,25,5,55,17,20,70,10,10,5,46
+<a href="https://github.com/RosettaCommons/RFdiffusion"> RFdiffusion </a>,17,90,13,40,65,50,37,4,57,16,30,80,12,20,8,63
+<a href="https://github.com/blt2114/twisted_diffusion_sampler"> TDS </a>,25,60,15,20,85,35,34,9,42,22,25,30,15,15,20,25
+<a href="https://github.com/microsoft/evodiff">EvoDiff</a>,0,0,0,0,0,0,0,0,0,0,9,38,36,3,5,3
+<a href="https://github.com/bytedance/dplm">DPLM</a>,0,1,0,1,0,0,0,0,1,0,37,100,81,48,94,79
+<a href="https://github.com/evolutionaryscale/esm"> ESM3 (1.4B)* </a>,19,5,0,1,2,7,13,0,56,50,28,100,91,77,89,54

data_link/multi_state_prediction.csv

@@ -0,0 +1,17 @@
+Model,RMSDens N=10,RMSDens N=100,RMSDens N=500,RMSDens N=1000,RMSD Cluster 3 N=10,RMSD Cluster 3 N=100,RMSD Cluster 3 N=500,RMSD Cluster 3 N=1000,Pairwise RMSD,CA clash (%),CA break (%),PepBond break (%)
+<a href="https://github.com/bjing2016/EigenFold"> EigenFold </a>,1.56,1.5,1.47,1.46,2.54,2.48,2.46,2.46,0.85,1.4,4.3,NaN
+<a href="https://www.nature.com/articles/s41586-023-06832-9"> MSA-depth256</a>,1.57,1.54,1.52,1.52,2.51,2.47,2.45,2.45,0.2,0,0,9.2
+<a href="https://www.nature.com/articles/s41586-023-06832-9"> MSA-depth64</a>,1.6,1.54,1.51,1.5,2.48,2.4,2.35,2.33,0.55,0,0,7.9
+<a href="https://www.nature.com/articles/s41586-023-06832-9"> MSA-depth32</a>,1.67,1.53,1.45,1.41,2.39,2.21,1.93,1.87,2.14,0.6,0,10.6
+<a href="https://github.com/lujiarui/Str2Str">Str2Str-ODE (Tmax=0.15)</a>,2.36,2.19,2.1,2.08,3.03,2.68,2.6,2.56,1.86,0,0,13.9
+<a href="https://github.com/lujiarui/Str2Str">Str2Str-SDE (Tmax=0.15)</a>,2.83,2.48,2.28,2.25,3.42,2.92,2.52,2.48,3.6,0.3,0,16
+<a href="https://github.com/bjing2016/alphaflow">AlphaFlow-PDB</a>,1.53,1.45,1.42,1.41,2.48,2.43,2.41,2.4,0.86,0,0,13.2
+<a href="https://github.com/bjing2016/alphaflow">AlphaFlow-MD</a>,1.74,1.51,1.45,1.43,2.44,2.32,2.28,2.24,1.26,0,0.1,26.2
+<a href="https://github.com/bjing2016/alphaflow">ESMFlow-PDB</a>,1.61,1.49,1.44,1.42,2.47,2.41,2.37,2.35,0.74,0,0,6
+<a href="https://github.com/bjing2016/alphaflow">ESMFlow-MD</a>,1.66,1.5,1.41,1.4,2.49,2.29,2.2,2.18,1.17,0,0,14.3
+<a href="https://github.com/bytedance/ConfDiff">ConfDiff-Open-ClsFree</a>,1.65,1.48,1.41,1.37,2.56,2.3,2.16,2.03,1.77,0.5,0,5.5
+<a href="https://github.com/bytedance/ConfDiff">ConfDiff-Open-MD</a>,1.64,1.5,1.44,1.42,2.49,2.39,2.32,2.31,1.37,0.2,0,4.6
+<a href="https://github.com/bytedance/ConfDiff">ConfDiff-ESM-ClsFree</a>,1.58,1.45,1.41,1.39,2.5,2.39,2.35,2.33,1.52,0.5,0,7.5
+<a href="https://github.com/bytedance/ConfDiff">ConfDiff-ESM-MD</a>,1.61,1.47,1.42,1.4,2.45,2.32,2.26,2.24,1.42,0.1,0,5
+<a href="https://github.com/bytedance/ConfDiff">ConfDiff-ESM-Energy</a>,1.63,1.47,1.43,1.42,2.55,2.43,2.41,2.4,1.26,0.1,0,7.5
+<a href="https://github.com/bytedance/ConfDiff">ConfDiff-ESM-Force</a>,1.58,1.44,1.37,1.36,2.45,2.33,2.23,2.22,1.76,0.1,0,8.9

data_link/protein_folding.csv

@@ -0,0 +1,6 @@
+Model,TM-score ↑,RMSD ↓,GDT-TS ↑,lDDT ↑,CA clash (%) ↓,CA break (%) ↓,PepBond break (%) ↓
+<a href="https://github.com/google-deepmind/alphafold"> AlphaFold2 </a>,0.871,3.21,0.86,0.9,0.3,0,4.8
+<a href="https://github.com/aqlaboratory/openfold/"> OpenFold </a>,0.87,3.21,0.856,0.895,0.4,0,2
+<a href="https://github.com/uw-ipd/RoseTTAFold2"> RoseTTAFold2 </a>,0.859,3.52,0.845,0.888,0.3,0.2,5.5
+<a href="https://github.com/facebookresearch/esm"> ESMFold </a>,0.847,3.98,0.826,0.87,0.3,0,4.7
+<a href="https://github.com/bjing2016/EigenFold"> EigenFold* </a>,0.743,7.65,0.703,0.737,8,0.5,NaN

data_link/sequence_design.csv

@@ -0,0 +1,6 @@
+Model,ppl (L=100) ↓,pLDDT (L=100) ↑,pairwise TM (L=100) ↓,Max Clust. (L=100) ↑,Max TM (L=100) ↓,ppl (L=200) ↓,pLDDT (L=200) ↑,pairwise TM (L=200) ↓,Max Clust. (L=200) ↑,Max TM (L=200) ↓,ppl (L=300) ↓,pLDDT (L=300) ↑,pairwise TM (L=300) ↓,Max Clust. (L=300) ↑,Max TM (L=300) ↓,ppl (L=500) ↓,pLDDT (L=500) ↑,pairwise TM (L=500) ↓,Max Clust. (L=500) ↑,Max TM (L=500) ↓
+Native Seqs,NaN,68.46,0.55,0.75,NaN,NaN,61.91,0.49,0.78,NaN,NaN,61.49,0.51,0.85,NaN,NaN,62.95,0.51,0.78,NaN
+<a href="https://github.com/enijkamp/progen2">Progen 2 (700M)</a>,8.28,64,0.42,0.94,0.64,5.68,69.91,0.4,0.91,0.69,6.25,65.69,0.42,0.93,0.66,4.27,61.45,0.32,0.95,0.68
+<a href="https://github.com/microsoft/evodiff">EvoDiff</a>,16.89,50.2,0.43,0.98,0.69,17.28,50.66,0.36,1,0.71,17.13,45.14,0.31,1,0.68,16.51,43.14,0.31,1,0.69
+<a href="https://github.com/bytedance/dplm">DPLM (650M)</a>,6.21,85.38,0.5,0.8,0.74,4.61,93.54,0.54,0.7,0.91,3.47,93.07,0.57,0.63,0.91,3.33,87.73,0.43,0.85,0.85
+<a href="https://github.com/evolutionaryscale/esm">ESM3 (1.4B)</a>,14.79,54.26,0.45,0.9,0.68,12.96,58.45,0.35,1,0.8,14.59,48.08,0.32,1,0.75,11.1,52.17,0.3,1,0.54

data_link/structure_design.csv

@@ -0,0 +1,10 @@
+Model,scTM (L=50) ↑,scRMSD (L=50) ↓,Max TM (L=50) ↓,pairwise TM (L=50) ↓,Max Clust. (L=50) ↑,scTM (L=100) ↑,scRMSD (L=100) ↓,Max TM (L=100) ↓,pairwise TM (L=100) ↓,Max Clust. (L=100) ↑,scTM (L=300) ↑,scRMSD (L=300) ↓,Max TM (L=300) ↓,pairwise TM (L=300) ↓,Max Clust. (L=300) ↑,scTM (L=500) ↑,scRMSD (L=500) ↓,Max TM (L=500) ↓,pairwise TM (L=500) ↓,Max Clust. (L=500) ↑
+Native PDBs,0.91,0.74,NaN,0.29,0.66,0.96,0.67,NaN,0.3,0.77,0.97,0.82,NaN,0.28,0.77,0.97,1.07,NaN,0.29,0.8
+<a href="https://github.com/RosettaCommons/RFdiffusion">RFdiffusion</a>,0.95,0.45,0.65,0.58,0.67,0.98,0.48,0.76,0.41,0.32,0.96,1.03,0.64,0.36,0.65,0.79,5.6,0.62,0.33,0.89
+<a href="https://github.com/microsoft/protein-frame-flow">FrameFlow</a>,0.91,0.58,0.75,0.68,0.39,0.94,0.7,0.72,0.55,0.49,0.92,1.95,0.65,0.43,0.88,0.61,7.92,0.61,0.4,0.92
+<a href="https://github.com/generatebio/chroma">Chroma</a>,0.85,1.05,0.59,0.29,0.48,0.89,1.27,0.7,0.35,0.59,0.87,2.47,0.66,0.36,0.67,0.72,6.71,0.6,0.29,0.99
+<a href="https://github.com/jasonkyuyim/se3_diffusion">FrameDiff(latest)</a>,0.85,1,0.67,0.35,0.64,0.9,1.23,0.71,0.52,0.11,0.87,2.73,0.69,0.48,0.21,0.63,9.52,0.58,0.4,0.52
+<a href="https://github.com/DreamFold/FoldFlow">FoldFlow1(sfm)</a>,0.9,0.67,0.68,0.63,0.48,0.87,1.34,0.65,0.49,0.83,0.45,9.04,0.54,0.39,1,0.37,13.04,0.53,0.37,1
+<a href="https://github.com/DreamFold/FoldFlow">FoldFlow1(base)</a>,0.79,1.19,0.66,0.53,0.76,0.81,1.7,0.62,0.48,0.83,0.43,9.56,0.54,0.39,0.98,0.35,13.2,0.52,0.39,1
+<a href="https://github.com/DreamFold/FoldFlow">FoldFlow1(ot)</a>,0.83,1.1,0.65,0.53,0.77,0.83,1.6,0.64,0.48,0.81,0.54,8.21,0.58,0.41,0.94,0.37,12.48,0.51,0.35,1
+<a href="https://github.com/aqlaboratory/genie2">Genie</a>,0.57,3.12,0.57,0.32,0.9,0.69,3.38,0.59,0.31,0.96,0.27,20.37,0.3,0.23,1,0.25,26.08,0.22,0.23,1

data_rank/antibody_design.csv

@@ -0,0 +1,2 @@
+Model,AAR ↑,RMSD ↓,TM-score ↑,Binding Energy ↓,SeqSim-outer ↓,SeqSim-inner ↑,PHR (%) ↓,CN-score ↑,Clashes-inner ↓,Clashes-outer ↓,SeqNat ↑,Total Energy ↓,scRMSD ↓
+0,-1,1,-1,1,1,0,1,-1,1,1,-1,1,1

data_rank/antibody_design_RankTerms.csv

@@ -1,2 +0,0 @@
-AAR ↑,RMSD ↓,TM-score ↑,Binding Energy ↓,SeqSim-outer ↓,SeqSim-inner ↑,PHR (%) ↓,CN-score ↑,Clashes-inner ↓,Clashes-outer ↓,SeqNat ↑,Total Energy ↓,scRMSD ↓
--1,1,-1,1,1,0,1,-1,1,1,-1,1,1

data_rank/co_design.csv

@@ -0,0 +1,2 @@
+Model,scTM (L=100) ↑,scRMSD (L=100) ↓,Max Clust. (L=100) ↑,Max TM (L=100) ↓,scTM (L=200) ↑,scRMSD (L=200) ↓,Max Clust. (L=200) ↑,Max TM (L=200) ↓,scTM (L=300) ↑,scRMSD (L=300) ↓,Max Clust. (L=300) ↑,Max TM (L=300) ↓,scTM (L=500) ↑,scRMSD (L=500) ↓,Max Clust. (L=500) ↑,Max TM (L=500) ↓
+0,-1,1,-1,1,-1,1,-1,1,-1,1,-1,1,-1,1,-1,1

data_rank/conformation_prediction.csv

@@ -0,0 +1,2 @@
+Model,Pairwise RMSD,*RMSF,Pearson r on Pairwise RMSD ↑,Pearson r on *Global RMSF ↑,Pearson r on *Per target RMSF ↑,*RMWD ↓,MD PCA W2 ↓,Joint PCA W2 ↓,PC sim > 0.5% ↑,Weak contacts J ↑,Transient contacts J ↑,*Exposed residue J ↑,*Exposed MI matrix ρ ↑,CA break % ↓,CA clash % ↓,PepBond break % ↓
+0,-1,0,0,0,-1,1,1,1,0,0,0,0,0,0,0,0

data_rank/inverse_folding.csv

@@ -0,0 +1,2 @@
+Model,CASP AAR ↑,CAMEO AAR ↑,scTM (L=100) ↑,pLDDT (L=100) ↑,scTM (L=200) ↑,pLDDT (L=200) ↑,scTM (L=300) ↑,pLDDT (L=300) ↑,scTM (L=400) ↑,pLDDT (L=400) ↑,scTM (L=500) ↑,pLDDT (L=500) ↑
+0,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1

data_rank/motif_scaffolding.csv

@@ -0,0 +1,2 @@
+Model,1QJG,2KL8,4JHW,4ZYP,5IUS,5TPN,5TRV,5WN9,6EXZ,7MRX,3IXT,1BCF,1PRW,1YCR,5YUI,6E6R
+0,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1

data_rank/multi_state_prediction.csv

@@ -0,0 +1,2 @@
+Model,RMSDens N=10,RMSDens N=100,RMSDens N=500,RMSDens N=1000,RMSD Cluster 3 N=10,RMSD Cluster 3 N=100,RMSD Cluster 3 N=500,RMSD Cluster 3 N=1000,Pairwise RMSD,CA clash (%),CA break (%),PepBond break (%)
+0,0,0,0,1,0,0,0,1,-1,0,0,0

data_rank/protein_folding.csv

@@ -0,0 +1,2 @@
+Model,TM-score ↑,RMSD ↓,GDT-TS ↑,lDDT ↑,CA clash (%) ↓,CA break (%) ↓,PepBond break (%) ↓
+0,-1,1,-1,-1,0,0,0

data_rank/sequence_design.csv

@@ -0,0 +1,2 @@
+Model,ppl (L=100) ↓,pLDDT (L=100) ↑,pairwise TM (L=100) ↓,Max Clust. (L=100) ↑,Max TM (L=100) ↓,ppl (L=200) ↓,pLDDT (L=200) ↑,pairwise TM (L=200) ↓,Max Clust. (L=200) ↑,Max TM (L=200) ↓,ppl (L=300) ↓,pLDDT (L=300) ↑,pairwise TM (L=300) ↓,Max Clust. (L=300) ↑,Max TM (L=300) ↓,ppl (L=500) ↓,pLDDT (L=500) ↑,pairwise TM (L=500) ↓,Max Clust. (L=500) ↑,Max TM (L=500) ↓
+0,1,-1,1,-1,1,1,-1,1,-1,1,1,-1,1,-1,1,1,-1,1,-1,1

data_rank/structure_design.csv

@@ -0,0 +1,2 @@
+Model,scTM (L=50) ↑,scRMSD (L=50) ↓,Max TM (L=50) ↓,pairwise TM (L=50) ↓,Max Clust. (L=50) ↑,scTM (L=100) ↑,scRMSD (L=100) ↓,Max TM (L=100) ↓,pairwise TM (L=100) ↓,Max Clust. (L=100) ↑,scTM (L=300) ↑,scRMSD (L=300) ↓,Max TM (L=300) ↓,pairwise TM (L=300) ↓,Max Clust. (L=300) ↑,scTM (L=500) ↑,scRMSD (L=500) ↓,Max TM (L=500) ↓,pairwise TM (L=500) ↓,Max Clust. (L=500) ↑
+0,-1,1,1,1,-1,-1,1,1,1,-1,-1,1,1,1,-1,-1,1,1,1,-1