def build(self, ligands, protein = None):
        if protein:
            self.set_protein(protein)
        else:
            protein = self.protein
        protein_dict = protein.atom_dict
        desc = None
        for mol in ligands:
            mol_dict = mol.atom_dict
            vec = np.array([], dtype=float)
            vec = tuple()
            # Vina
            ### TODO: Asynchronous output from vina, push command to score and retrieve at the end?
            ### TODO: Check if ligand has vina scores
            vec += tuple(self.vina.build(mol, single=True).flatten())

            # Close Contacts (<4A)
            vec += tuple(self.cc_4.build(mol, single=True).flatten())

            # Electrostatics (<4A)
            ele_types = (('A', 'A'), ('A', 'C'), ('A', 'CL'), ('A', 'F'), ('A', 'FE'), ('A', 'HD'), ('A', 'MG'), ('A', 'MN'), ('A', 'N'), ('A', 'NA'), ('A', 'OA'), ('A', 'SA'), ('A', 'ZN'), ('BR', 'C'), ('BR', 'HD'), ('BR', 'OA'), ('C', 'C'), ('C', 'CL'), ('C', 'F'), ('C', 'HD'), ('C', 'MG'), ('C', 'MN'), ('C', 'N'), ('C', 'NA'), ('C', 'OA'), ('C', 'SA'), ('C', 'ZN'), ('CL', 'FE'), ('CL', 'HD'), ('CL', 'MG'), ('CL', 'N'), ('CL', 'OA'), ('CL', 'ZN'), ('F', 'HD'), ('F', 'N'), ('F', 'OA'), ('F', 'SA'), ('F', 'ZN'), ('FE', 'HD'), ('FE', 'N'), ('FE', 'OA'), ('HD', 'HD'), ('HD', 'I'), ('HD', 'MG'), ('HD', 'MN'), ('HD', 'N'), ('HD', 'NA'), ('HD', 'OA'), ('HD', 'P'), ('HD', 'S'), ('HD', 'SA'), ('HD', 'ZN'), ('MG', 'NA'), ('MG', 'OA'), ('MN', 'N'), ('MN', 'OA'), ('N', 'N'), ('N', 'NA'), ('N', 'OA'), ('N', 'SA'), ('N', 'ZN'), ('NA', 'OA'), ('NA', 'SA'), ('NA', 'ZN'), ('OA', 'OA'), ('OA', 'SA'), ('OA', 'ZN'), ('S', 'ZN'), ('SA', 'ZN'), ('A', 'BR'), ('A', 'I'), ('A', 'P'), ('A', 'S'), ('BR', 'N'), ('BR', 'SA'), ('C', 'FE'), ('C', 'I'), ('C', 'P'), ('C', 'S'), ('CL', 'MN'), ('CL', 'NA'), ('CL', 'P'), ('CL', 'S'), ('CL', 'SA'), ('CU', 'HD'), ('CU', 'N'), ('FE', 'NA'), ('FE', 'SA'), ('I', 'N'), ('I', 'OA'), ('MG', 'N'), ('MG', 'P'), ('MG', 'S'), ('MG', 'SA'), ('MN', 'NA'), ('MN', 'P'), ('MN', 'S'), ('MN', 'SA'), ('N', 'P'), ('N', 'S'), ('NA', 'P'), ('NA', 'S'), ('OA', 'P'), ('OA', 'S'), ('P', 'S'), ('P', 'SA'), ('P', 'ZN'), ('S', 'SA'), ('SA', 'SA'))
            ele_rec_types, ele_lig_types = zip(*ele_types)
            ele_mol_atoms = atoms_by_type(mol_dict, ele_lig_types, 'atom_types_ad4')
            ele_rec_atoms = atoms_by_type(protein_dict, ele_rec_types, 'atom_types_ad4')
            ele = tuple()
            for r_t, m_t in ele_types:
                mol_ele_dict, rec_ele_dict = interactions.close_contacts(ele_mol_atoms[m_t], ele_rec_atoms[r_t], 4)
                if len(mol_ele_dict) and len(rec_ele_dict):
                    ele += (mol_ele_dict['charge'] * rec_ele_dict['charge']/ np.sqrt((mol_ele_dict['coords'] - rec_ele_dict['coords'])**2).sum(axis=-1) * 138.94238460104697e4).sum(), # convert to J/mol
                else:
                    ele += 0,
            vec += tuple(ele)

            # Ligand Atom Types
            ligand_atom_types = ['A', 'BR', 'C', 'CL', 'F', 'HD', 'I', 'N', 'NA', 'OA', 'P', 'S', 'SA']
            atoms = atoms_by_type(mol_dict, ligand_atom_types, 'atom_types_ad4')
            atoms_counts = [len(atoms[t]) for t in ligand_atom_types]
            vec += tuple(atoms_counts)

            # Close Contacts (<2.5A)
            vec += tuple(self.cc_25.build(mol, single=True).flatten())

            # H-Bonds (<4A)
            hbond_mol, hbond_rec, strict = interactions.hbond(mol, protein, 4)
            # Retain only strict hbonds
            hbond_mol = hbond_mol[strict]
            hbond_rec = hbond_rec[strict]
            backbone = hbond_rec['isbackbone']
            alpha = hbond_rec['isalpha']
            beta = hbond_rec['isbeta']
            other = ~alpha & ~beta
            donor_mol = hbond_mol['isdonor']
            donor_rec = hbond_rec['isdonor']
            hbond_vec = ((donor_mol & backbone & alpha).sum(), (donor_mol & backbone & beta).sum(), (donor_mol & backbone & other).sum(),
                        (donor_mol & ~backbone & alpha).sum(), (donor_mol & ~backbone & beta).sum(), (donor_mol & ~backbone & other).sum(),
                        (donor_rec & backbone & alpha).sum(), (donor_rec & backbone & beta).sum(), (donor_rec & backbone & other).sum(),
                        (donor_rec & ~backbone & alpha).sum(), (donor_rec & ~backbone & beta).sum(), (donor_rec & ~backbone & other).sum())
            vec += tuple(hbond_vec)

            # Hydrophobic contacts (<4A)
            hydrophobic = interactions.hydrophobic_contacts(mol, protein, 4)[1]
            backbone = hydrophobic['isbackbone']
            alpha = hydrophobic['isalpha']
            beta = hydrophobic['isbeta']
            other = ~alpha & ~beta
            hyd_vec = ((backbone & alpha).sum(), (backbone & beta).sum(), (backbone & other).sum(),
                       (~backbone & alpha).sum(), (~backbone & beta).sum(), (~backbone & other).sum(), len(hydrophobic))
            vec += tuple(hyd_vec)

            # Pi-stacking (<7.5A)
            pi_mol, pi_rec, pi_paralel, pi_tshaped = interactions.pi_stacking(mol, protein, 7.5)
            alpha = pi_rec['isalpha'] & pi_paralel
            beta = pi_rec['isbeta'] & pi_paralel
            other = ~alpha & ~beta & pi_paralel
            pi_vec = (alpha.sum(), beta.sum(), other.sum())
            vec += tuple(pi_vec)

            # count T-shaped Pi-Pi interaction
            alpha = pi_rec['isalpha'] & pi_tshaped
            beta = pi_rec['isbeta'] & pi_tshaped
            other = ~alpha & ~beta & pi_tshaped
            pi_t_vec = (alpha.sum(), beta.sum(), other.sum())

            # Pi-cation (<6A)
            pi_rec, cat_mol, strict = interactions.pi_cation(protein, mol, 6)
            alpha = pi_rec['isalpha'] & strict
            beta = pi_rec['isbeta'] & strict
            other = ~alpha & ~beta & strict
            pi_cat_vec = (alpha.sum(), beta.sum(), other.sum())

            pi_mol, cat_rec, strict = interactions.pi_cation(mol, protein, 6)
            alpha = cat_rec['isalpha'] & strict
            beta = cat_rec['isbeta'] & strict
            other = ~alpha & ~beta & strict
            pi_cat_vec += (alpha.sum(), beta.sum(), other.sum())

            vec += tuple(pi_cat_vec)

            # T-shape (perpendicular Pi's) (<7.5A)
            vec += tuple(pi_t_vec)

            # Active site flexibility (<4A)
            acitve_site = interactions.close_contacts(mol_dict, protein_dict, 4)[1]
            backbone = acitve_site['isbackbone']
            alpha = acitve_site['isalpha']
            beta = acitve_site['isbeta']
            other = ~alpha & ~beta
            as_flex = ((backbone & alpha).sum(), (backbone & beta).sum(), (backbone & other).sum(),
                       (~backbone & alpha).sum(), (~backbone & beta).sum(), (~backbone & other).sum(), len(acitve_site))
            vec += tuple(as_flex)

            # Salt bridges (<5.5)
            salt_bridges = interactions.salt_bridges(mol, protein, 5.5)[1]
            vec += (salt_bridges['isalpha'].sum(), salt_bridges['isbeta'].sum(),
                                   (~salt_bridges['isalpha'] & ~salt_bridges['isbeta']).sum(), len(salt_bridges))

            # Rotatable bonds
            vec += mol.num_rotors,

            if desc is None:
                desc = np.zeros(len(vec), dtype=float)
            desc = np.vstack((desc, np.array(vec, dtype=float)))

        return desc[1:]
test that we handle hard links properly
dataset/ETHPy150Open oddt/oddt/oddt/scoring/descriptors/binana.py/binana_descriptor.build--crossover with--dataset/ETHPy150Open borgbackup/borg/borg/testsuite/upgrader.py/test_hardlink