chemprop.nn.message_passing.mol_atom_bond ========================================= .. py:module:: chemprop.nn.message_passing.mol_atom_bond Classes ------- .. autoapisummary:: chemprop.nn.message_passing.mol_atom_bond.MABBondMessagePassing chemprop.nn.message_passing.mol_atom_bond.MABAtomMessagePassing Module Contents --------------- .. py:class:: MABBondMessagePassing(d_v = DEFAULT_ATOM_FDIM, d_e = DEFAULT_BOND_FDIM, d_h = DEFAULT_HIDDEN_DIM, bias = False, depth = 3, dropout = 0.0, activation = Activation.RELU, undirected = False, d_vd = None, d_ed = None, V_d_transform = None, E_d_transform = None, graph_transform = None, return_vertex_embeddings = True, return_edge_embeddings = True) Bases: :py:obj:`chemprop.nn.message_passing.mixins._BondMessagePassingMixin`, :py:obj:`_MABMessagePassingBase` A :class:`MABBondMessagePassing` encodes a batch of molecular graphs by passing messages along directed bonds. It implements the following operation: .. math:: h_{vw}^{(0)} &= \tau \left( \mathbf W_i(e_{vw}) \right) \\ m_{vw}^{(t)} &= \sum_{u \in \mathcal N(v)\setminus w} h_{uv}^{(t-1)} \\ h_{vw}^{(t)} &= \tau \left(h_v^{(0)} + \mathbf W_h m_{vw}^{(t-1)} \right) \\ m_v^{(T)} &= \sum_{w \in \mathcal N(v)} h_w^{(T-1)} \\ h_v^{(T)} &= \tau \left (\mathbf W_o \left( x_v \mathbin\Vert m_{v}^{(T)} \right) \right), where :math:`\tau` is the activation function; :math:`\mathbf W_i`, :math:`\mathbf W_h`, and :math:`\mathbf W_o` are learned weight matrices; :math:`e_{vw}` is the feature vector of the bond between atoms :math:`v` and :math:`w`; :math:`x_v` is the feature vector of atom :math:`v`; :math:`h_{vw}^{(t)}` is the hidden representation of the bond :math:`v \rightarrow w` at iteration :math:`t`; :math:`m_{vw}^{(t)}` is the message received by the bond :math:`v \to w` at iteration :math:`t`; and :math:`t \in \{1, \dots, T-1\}` is the number of message passing iterations. .. py:method:: setup(d_v = DEFAULT_ATOM_FDIM, d_e = DEFAULT_BOND_FDIM, d_h = DEFAULT_HIDDEN_DIM, d_vd = None, d_ed = None, bias = False) setup the weight matrices used in the message passing update functions :param d_v: the vertex feature dimension :type d_v: int :param d_e: the edge feature dimension :type d_e: int :param d_h: the hidden dimension during message passing :type d_h: int, default=300 :param d_vd: the dimension of additional vertex descriptors that will be concatenated to the hidden features before readout, if any :type d_vd: int | None, default=None :param d_ed: the dimension of additional edge descriptors that will be concatenated to the hidden features before readout, if any :type d_ed: int | None, default=None :param bias: whether to add a learned bias to the matrices :type bias: bool, default=False :returns: **W_i, W_h, W_vo, W_vd, W_eo, W_ed** -- the input, hidden, output, and descriptor weight matrices, respectively, used in the message passing update functions. The descriptor weight matrix is `None` if no vertex dimension is supplied :rtype: tuple[nn.Module, nn.Module, nn.Module, nn.Module | None] .. py:class:: MABAtomMessagePassing(d_v = DEFAULT_ATOM_FDIM, d_e = DEFAULT_BOND_FDIM, d_h = DEFAULT_HIDDEN_DIM, bias = False, depth = 3, dropout = 0.0, activation = Activation.RELU, undirected = False, d_vd = None, d_ed = None, V_d_transform = None, E_d_transform = None, graph_transform = None, return_vertex_embeddings = True, return_edge_embeddings = True) Bases: :py:obj:`chemprop.nn.message_passing.mixins._AtomMessagePassingMixin`, :py:obj:`_MABMessagePassingBase` A :class:`MABAtomMessagePassing` encodes a batch of molecular graphs by passing messages along atoms. It implements the following operation: .. math:: h_v^{(0)} &= \tau \left( \mathbf{W}_i(x_v) \right) \\ m_v^{(t)} &= \sum_{u \in \mathcal{N}(v)} h_u^{(t-1)} \mathbin\Vert e_{uv} \\ h_v^{(t)} &= \tau\left(h_v^{(0)} + \mathbf{W}_h m_v^{(t-1)}\right) \\ m_v^{(T)} &= \sum_{w \in \mathcal{N}(v)} h_w^{(T-1)} \\ h_v^{(T)} &= \tau \left (\mathbf{W}_o \left( x_v \mathbin\Vert m_{v}^{(T)} \right) \right), where :math:`\tau` is the activation function; :math:`\mathbf{W}_i`, :math:`\mathbf{W}_h`, and :math:`\mathbf{W}_o` are learned weight matrices; :math:`e_{vw}` is the feature vector of the bond between atoms :math:`v` and :math:`w`; :math:`x_v` is the feature vector of atom :math:`v`; :math:`h_v^{(t)}` is the hidden representation of atom :math:`v` at iteration :math:`t`; :math:`m_v^{(t)}` is the message received by atom :math:`v` at iteration :math:`t`; and :math:`t \in \{1, \dots, T\}` is the number of message passing iterations. .. py:method:: setup(d_v = DEFAULT_ATOM_FDIM, d_e = DEFAULT_BOND_FDIM, d_h = DEFAULT_HIDDEN_DIM, d_vd = None, d_ed = None, bias = False) setup the weight matrices used in the message passing update functions :param d_v: the vertex feature dimension :type d_v: int :param d_e: the edge feature dimension :type d_e: int :param d_h: the hidden dimension during message passing :type d_h: int, default=300 :param d_vd: the dimension of additional vertex descriptors that will be concatenated to the hidden features before readout, if any :type d_vd: int | None, default=None :param d_ed: the dimension of additional edge descriptors that will be concatenated to the hidden features before readout, if any :type d_ed: int | None, default=None :param bias: whether to add a learned bias to the matrices :type bias: bool, default=False :returns: **W_i, W_h, W_vo, W_vd, W_eo, W_ed** -- the input, hidden, output, and descriptor weight matrices, respectively, used in the message passing update functions. The descriptor weight matrix is `None` if no vertex dimension is supplied :rtype: tuple[nn.Module, nn.Module, nn.Module, nn.Module | None]