An approval prediction apparatus for drug development comprising an output unit, a storage unit, and a control unit, wherein the storage unit includes: a similarity network information storage unit configured for storing similarity network information on a protein similarity network that is constructed according to the similarity between proteins, said similarity between proteins being found with a protein signature-based algorithm determining the similarity between two sequence information, said similarity network information being determined as a graph comprising: - nodes, each node representing a protein, and - edges, such that two nodes are connected by an edge only if the nodes share considerable protein sequence similarity; a drug target storage unit configured for storing drug information containing approval attributes of drugs on approval or rejection and protein information on the proteins targeted by the drugs in association with each other; and an interaction network information storage unit configured for storing interaction network information on a protein-protein interaction network that is constructed based on interactions between pre-selected proteins, said interaction between proteins meaning that said proteins are similar, said interaction network information being determined as a graph comprising: - nodes, each node representing a protein, and - edges such that two nodes are connected if the corresponding proteins have an interaction; and the control unit includes: a similarity centrality measure calculating unit configured for calculating, based on the similarity network information stored in the similarity network information storage unit, similarity centrality measures that are centrality measures containing: - a degree centrality, defined as an index representing how much the node is directly connected to nodes in the similarity network, - a betweenness centrality B(v), using the following expression formed of S ij denoting the number of shortest paths between a node i and a node j, and S ij (v) denoting the fraction of shortest paths passing through a node v: B v = ∑ S ij v S ij with i ≠ j, v ≠ i and v ≠ j, - a closeness centrality C(v), using the following expression formed of d(v,i) denoting the distance represented at the step between a node v and the node i: C v = 1 ∑ d v i with i ≠ v, and - a Burt's constraint C(i) of the proteins included in the protein similarity network, using the following expression formed of p iq p qj denoting a product of the proportional strength of the node j's relationship with the node i and the proportional strength of the node j's relationship with the node q: C i = ∑ j p ij + ∑ q p ip p qj 2 with q ≠ i, j and j ≠ i; an interaction centrality measure calculating unit configured for calculating, based on the interaction network information stored in the interaction network information storage unit, interaction centrality measures that are centrality measures containing said degree centrality, betweenness centrality calculated with expression, closeness centrality calculated with expression, and Burt's constraint of the proteins included in the protein-protein interaction network calculated with expression; a rejection score calculating unit configured for calculating during the drug development a rejection score that represents a probability of a compound to be validated to be classified as a rejected drug, using classifiers that use, as training data, the approval attributes of the respective drugs stored in the drug target storage unit, the sum and average of the similarity centrality measures per target for each drug that are calculated by the similarity centrality measure calculating unit, and the sum and average of the interaction centrality measures per target for each drug that are calculated by the interaction centrality measure calculating unit, said similarity centrality measures used as parameters for several machine learning classifiers; and a rejection score outputting unit configured for outputting, via the output unit, the rejection score that is calculated by the rejection score calculating unit. - nodes, each node representing a protein, and - edges, such that two nodes are connected by an edge only if the nodes share considerable protein sequence similarity; - nodes, each node representing a protein, and - edges such that two nodes are connected if the corresponding proteins have an interaction; and - a degree centrality, defined as an index representing how much the node is directly connected to nodes in the similarity network, - a betweenness centrality B(v), using the following expression formed of S ij denoting the number of shortest paths between a node i and a node j, and S ij (v) denoting the fraction of shortest paths passing through a node v: B v = ∑ S ij v S ij with i ≠ j, v ≠ i and v ≠ j, - a closeness centrality C(v), using the following expression formed of d(v,i) denoting the distance represented at the step between a node v and the node i: C v = 1 ∑ d v i with i ≠ v, and - a Burt's constraint C(i) of the proteins included in the protein similarity network, using the following expression formed of p iq p qj denoting a product of the proportional strength of the node j's relationship with the node i and the proportional strength of the node j's relationship with the node q: C i = ∑ j p ij + ∑ q p ip p qj 2 with q ≠ i, j and j ≠ i;